# Welcome to the Principia Cybernetica Web

Author: Editors
Updated: Mar 23, 1998
Filename: DEFAULT.html

This is the web server of the Principia Cybernetica Project (PCP), an international organization. PCP tries to tackle age-old philosophical questions with the help of the most recent cybernetic theories and technologies. Stated more precisely, the Project's aim is the computer-supported collaborative development of an evolutionary-systemic philosophy.

To get started, there is an introduction with background and motivation, and an overview, summarizing the project as a whole.

## Main subjects

MetaSystem Transition Theory
PCP's theoretical results, including epistemology, metaphysics, ethics, concepts, principles, memetics, and the history and future of evolution.

Project Organization
details the people involved, conferences, publications, methods of collaboration, ways of contributing, and mailing lists used by PCP.

Learning, Brain-like Webs
our experimental research on self-organizing networks, based on the "Global Brain" metaphor

Cybernetics and Systems Theory
an extensive collection of reference material on this domain, including bibliographies, associations, journals, a dictionary, and "The Macroscope", a complete book.

Related Sites
extensive lists of links covering systems, cybernetics, complexity, man-machine interaction, cognition, philosophy, evolution, networking, knowledge integration, and future developments.

Other Info
this server also hosts some pages that are not part of PCP: the Center "Leo Apostel", the Association for the Foundations of Science, Language and Cognition (AFOS), the International Quantum Structures Association (IQSA) and Belgium: Overview

The following tools will help you to quickly find your way around the more than thousand pages of Principia Cybernetica Web:

Recent Changes
"What's new" on this server
Searchable index
keyword search of all documents (titles and full-text).
a long hierarchical outline, which provides a "standard ordering" through the main material.
jump to an arbitrary node. Useful to get unusual suggestions for areas to explore.
nodes ordered according to popularity, and other usage statistics for the server (out-of-date).
Map
the picture below is a clickable map of the most important nodes of the PCP hierarchy.

Principia Cybernetica Web is one of the oldest (registered July '93), best organized, and largest, fully connected hypertexts on the Net. It contains over 1400 "nodes" (hypertext pages), numerous papers, and even complete books. Some 8000 files (mostly text documents) are consulted every day on this server, that is, more than 2 million per year. Some ten thousand links point to documents in this web.

Although Principia Cybernetica Web has received very positive reviews, the work is of course never finished. The material in this web is continuously being added to and improved. Nodes followed by the mention "[empty]" don't contain any text yet, only a menu of linked nodes. Some important results have not yet been converted to hypertext, but may be found in the papers in our FTP-archive.

Comments about content and presentation of the information are appreciated. If you have any technical problems, questions or suggestions on our Web, please contact the "Webmaster", Francis Heylighen (PCP@vub.ac.be). Comments about the content of a node can be addressed to its author(s). You can also directly annotate each node separately, or add general comments to the User Annotations.

We apologize for difficulties you might have in getting files from this server: Internet connections between Belgium and especially America are often overloaded. Try to avoid the most busy periods: 15.00 to 0.00 hrs (European time), i.e. 9.00 to 18.00 (US East Coast) or 6.00 to 15.00 (US West Coast), on weekdays. We would like to establish a mirror site in the US in order to avoid this problem in the future: proposals welcome! At present we only have a Belgian back-up FTP-server with WWW documents at ftp.vub.ac.be for emergencies, but it is not kept up-to-date. These servers are part of the network of the Free University of Brussels.

If you plan to regularly consult this server, you might keep a copy of this home page on your own computer.

# Introduction to Principia Cybernetica

Author: Heylighen, Joslyn, Turchin,
Updated: Apr 1, 1996
Filename: INTRO.html

PCP is about Philosophy. But what is philosophy? Philosophy intends to answer the eternal questions: Who am I? Where do I come from? Where am I going to? What is knowledge? What is truth? What are good and evil? What is the meaning of life?
But there is a huge literature on philosophy. What is new here?

Every time has its own approach to these eternal philosophical questions, deriving from its knowledge and technology. We hold that in our time, the age of information, it is systems science and cybernetics, as the general sciences of organization and communication, that can provide the basis for contemporary philosophy. Therefore, this philosophical system is derived from, and further develops, the basic principles of cybernetics.

Moreover, we start from the thesis that systems at all levels have been constructed by evolution, which we see as a continuing process of self-organization, based on variation and natural selection of the "fittest" configuration. Evolution continuously creates complexity and makes systems more adaptive by giving them better control over their environments. We consider the emergence of a new level of control as the quantum of evolution, and call it a "metasystem transition".

As cybernetic theory informs our philosophy, so cybernetic technology lets us do things that philosophers of other times could only dream of. Using computer technology, we develop a large philosophical text from many nodes which are linked together with different relationships. Readers can navigate among the many concepts, guided by their individual understanding and interests. Disparate material can be integrated together while being written and read by collaborators from all around the world, undergoing variation and selection. Thus we apply theories about the evolution of cybernetic systems to the practical development of this very system of philosophy.

We hold that PCP is more than an interesting experiment, and that there is an acute need for an approach similar to PCP. The on-going explosion and fragmentation of knowledge demands a renewed effort at integration. This has always been the dream of the systems theorists; all they lacked was the appropriate technology to attack the complexity of the task.

PCP draws its inspiration from many predecessors in intellectual history, including philosophers, systems scientists and cyberneticians, and others who have tried to collaboratively develop complex systems of thought.

This effort has been on-going since 1989, and is now in the stage of implementation (see our history). Of course, the task is enormous, and we are still beginning. If you are really interested in our Project, we invite you to join our efforts and become a contributor.

For further introductory reading, see the following documents:

1. A Short Introduction to the Principia Cybernetica Project (1991 paper)
2. Workbook of the 1st Principia Cybernetica Workshop (short papers and abstracts, 1991)
3. Principia Cybernetica: an Introduction, a view of PCP by an outsider, Koen Van Damme, with fragments of interviews with C. Joslyn and V. Turchin (1996)
4. A Dialogue on Metasystem Transition, V. Turchin's introductory overview of some of the basic concepts of the PCP philosophy (1995)

# Eternal Philosophical Questions

Author: F. Heylighen,
Updated: Nov 5, 1997
Filename: ETERQUES.html

The Principia Cybernetica Project aims to develop a complete philosophical system or "world view". This philosophy tries to answer the fundamental questions, which every person reflecting about the world and his or her place in it has been asking throughout the ages. The PCP philosophy is organized as a complex network of mutually dependent concepts and principles. Therefore, the answers to these questions are scattered throughout the different "nodes" of the web.

The present document brings these different questions and answers together, in the form of a "FAQ" (Frequently Asked Questions). The answers given here are by necessity short. They barely scratch the surface of a profound and complex issue. However, where available, we have included links to other documents which discuss the problem in more detail. The present document can be seen as a roadmap, which will help philosophically interested readers to better explore the Principia Cybernetica world view.

What is?
This question defines the domain of ontology. We believe that the fundamental stuff of being, the essence of the universe, consists of elementary processes or actions, rather than matter, energy or ideas. Complex organizations, such as atoms, molecules, space and time, living beings, minds and societies emerge out of these actions through the process of evolution.
Why is there something rather than nothing?
The universe arose spontaneously, through self-organizing evolution, based on the self-evident principles of variation and natural selection. Any possible variation (for example a "quantum fluctuation of the vacum") would be sufficient to set the self-organizing process in motion, thus generating a complex universe with its diverse components and structures.
Why is the world the way it is?
The specific state of the universe or the world in which we live is partially a historical accident, since evolution is an indeterministic process, partially the result of a lawful process of self-organization, which leads predictably to higher levels of organization through the mechanism of metasystem transition.
Where does it all come from?
We can reconstruct in some detail the subsequent stages in the evolution of the universe, leading from the Big Bang, elementary particles, atoms and molecules to living cells, multicellular organisms, animals, people and society. Thus the history of evolution, conceived as a sequence of metasystem transitions, tells us how and in which order all the different types of phenomena we see around us have arisen.
Where do we come from?
Humans evolved out of animals that had the capacity to learn associations from the environment, by additionally acquiring the capacity to autonomously control these associations, i.e. to think. Human thought is rooted in the emergence of symbolic language.
Who are we?
As far as we know, humans occupy the provisionally most advanced level in the hierarchy of metasystems. Our capacity for thought distinguishes us from the animals by giving us uniquely human characteristics, such as self-consciousness, tool making, imagination, planning, play, sense of humor and esthetic feelings.
Where are we going to?
The theory of metasystem transitions helps us to extrapolate present, on-going progress into the future. Recent developments point to a new metasystem transition which will bring us to a yet higher level of complexity or consciousness, transcending individual thought. This emergent level is perhaps best described by the metaphor of the social superorganism and its global brain.
What is the purpose of it all?
Evolution does not have a purpose, in the sense of a fixed goal to which it is advancing. However, although evolution is largely unpredictable, it is not random either. Selection can be seen as having the implicit goal of maximizing fitness. This implies a preferred direction of evolution, which is in practice characterized by increasing complexity and intelligence.
Is there a God?
Since the mechanisms of self-organizing evolution satisfactorily explain the origin and development of the universe, and our place in it, there is no need to postulate a personal God, in the sense of an agency outside of the universe, which created that universe. However, if you wish, you can consider the universe or the process of evolution itself as God-like, in the spirit of pantheism.
What is good and what is evil?
The evolutionary mechanism of natural selection makes an implicit distinction between "good" or "fit" situations (those which survive in the long term), and "bad" or "unfit" ones (those which are eliminated sooner or later). Therefore, we might equate good or higher values with anything that contributes to survival and the continuation of the process of evolution, and evil with anything that destroys, kills or thwarts the development of fit systems.
What is knowledge?
This question defines the domain of epistemology. Knowledge is the existence in a cybernetic system of a model, which allows that system to make predictions, that is, to anticipate processes in its environment. Thus, the system gets control over its environment. Such a model is a personal construction, not an objective reflection of outside reality.
What is truth?
There are no absolute truths. The truth of a theory is merely its power to produce predictions that are confirmed by observations. However, different theories can produce similar predictions without one of them being right and the other wrong. "True" knowledge is the one that best survives the natural selection for predictive power.
How should we act?
Effective action is based on a clear sense of goals or values, and a good model of the environment in which you try to reach these goals. By applying problem-solving methods (in the simplest case, just trial-and-error), you can explore your model to find the most efficient path from your present situation to your goal. You can then try out this action plan in practice, taking into account the feedback you get, in order to correct your course.
How can we be happy?
People are happy when they are "in control", that is, feel competent to satisfy their needs and reach their goals. Happiness is most common in societies which provide sufficient wealth, health care, education, personal freedom and equality. Happy people tend to be self-confident, open to experience and have good personal relations. Promoting these social and personal values should increase our overall quality of life.
Why cannot we live forever?
Evolution has predisposed us to age and die because fitness is achieved more easily by fast reproduction than by long life. Aging is the result of a variety of deterioration processes. Therefore, it is unlikely that we will achieve biological immortality in the near future, in spite of a constantly increasing life span. However, we can still aim for cybernetic immortality: survival of our mental organization, rather than our material body.
What is the meaning of life?
This question in a sense summarizes all previous questions. In essence, the meaning of life is to increase evolutionary fitness. This can be reformulated in more detail as: the purpose of (living) organization is to continuously increase future probabilities of encountering this same type of organization.

# Philosophy

Author: C. Joslyn,
Updated: Aug 1993
Filename: PHILOSI.html

definition according to Webster's dictionary.

We begin with the idea that philosophy is a kind of clear, deep thought; essentially putting our thought and language in order. This apparently analytic and linguistic understanding arises from the explicit recognition that all expression and communication, in particular all works of philosophy, the body of Principia Cybernetica, and this article itself, exist in a physical form as a series of symbol tokens in a particular modality and interpretable in a specific language and interpretational framework. It is impossible to consider philosophy in particular outside of the context of its processes and products. In that respect, philosophy must be understood as a process of philosophizing in which linguistic symbol tokens are produced and received. This includes the normal linguistic forms of speaking, hearing, reading, and writing, but also other linguistic forms such as diagrams, mathematics, and sign language. The authors of this paper philosophize as they write it; the readers philosophize as they read it. This article itself cannot have any existence "as philosophy" outside of this context of its production and/or reception.

What then distinguishes philosophical linguistic productions from any other? It is tempting to distinguish philosophy on the basis of its content, that is its referents, or what it is "about". Then we would believe, as some cybernetic philosophers have suggested \cite{BAA53a}, that philosophy is linguistic thought which refers to specific deep questions, e.g. about existence and knowledge, the nature of thought, and the ultimate good. We do not deny this, but do not believe that it is a good place to start in finding a definition.

Rather the focus on philosophizing as a process leads us to consider philosophy as any language conducted in a certain manner. In particular, whenever we deal with issues in depth, continually asking "why" and "how" to critically analyze underlying assumptions and move to the foundations of our complex knowledge structures, then that is necessarily philosophy. Thus we construct philosophy of language, of mind, or of law when we consider these specific subjects in their depth. Surely we could have a philosophy of plumbing or gum chewing should we wish.

As we proceed in the question asking mode towards deep thought and thus philosophy, then of course we are naturally drawn to the traditional philosophical questions outlined above. But what distinguishes them as the quintessential philosophical problems is their generality. Thus if we restrict ourselves specifically to (say) philosophy of law or plumbing, then perhaps we can avoid certain general philosophical issues. Philosophy per se is simply the result of philosophizing in an unrestricted domain of discourse.

# Epistemology, introduction

Author: F. Heylighen,
Updated: Sep 1993
Filename: EPISTEMI.html

Epistemology is the branch of philosophy that studies knowledge. It attempts to answer the basic question: what distinguishes true (adequate) knowledge from false (inadequate) knowledge? Practically, this questions translates into issues of scientific methodology: how can one develop theories or models that are better than competing theories? It also forms one of the pillars of the new sciences of cognition, which developed from the information processing approach to psychology, and from artificial intelligence, as an attempt to develop computer programs that mimic a human's capacity to use knowledge in an intelligent way.

When we look at the history of epistemology, we can discern a clear trend, in spite of the confusion of many seemingly contradictory positions. The first theories of knowledge stressed its absolute, permanent character, whereas the later theories put the emphasis on its relativity or situation-dependence, its continuous development or evolution, and its active interference with the world and its subjects and objects. The whole trend moves from a static, passive view of knowledge towards a more and more adaptive and active one.

Let us start with the Greek philosophers. In Plato's view knowledge is merely an awareness of absolute, universal Ideas or Forms, existing independent of any subject trying to apprehend to them. Though Aristotle puts more emphasis on logical and empirical methods for gathering knowledge, he still accepts the view that such knowledge is an apprehension of necessary and universal principles. Following the Renaissance, two main epistemological positions dominated philosophy: empiricism, which sees knowledge as the product of sensory perception, and rationalism which sees it as the product of rational reflection.

The implementation of empiricism in the newly developed experimental sciences led to a view of knowledge which is still explicitly or implicity held by many people nowadays: the reflection-correspondence theory. According to this view knowledge results from a kind of mapping or reflection of external objects, through our sensory organs, possibly aided by different observation instruments, to our brain or mind. Though knowledge has no a priori existence, like in Plato's conception, but has to be developed by observation, it is still absolute, in the sense that any piece of proposed knowledge is supposed to either truly correspond to a part of external reality, or not. In that view, we may in practice never reach complete or absolute knowledge, but such knowledge is somehow conceivable as a limit of ever more precise reflections of reality.

The following important theory developed in that period is the Kantian synthesis of rationalism and empiricism. According to Kant, knowledge results from the organization of perceptual data on the basis of inborn cognitive structures, which he calls "categories". Categories include space, time, objects and causality. This epistemology does accept the subjectivity of basic concepts, like space and time, and the impossibility to reach purely objective representations of things-in-themselves. Yet the a priori categories are still static or given.

The next stage of development of epistemology may be called pragmatic. Parts of it can be found in early twentieth century approaches, such as logical positivism, conventionalism, and the "Copenhagen interpretation" of quantum mechanics. This philosophy still dominates most present work in cognitive science and artificial intelligence. According to pragmatic epistemology, knowledge consists of models that attempt to represent the environment in such a way as to maximally simplify problem-solving. It is assumed that no model can ever hope to capture all relevant information, and even if such a complete model would exist, it would be too complicated to use in any practical way. Therefore we must accept the parallel existence of different models, even though they may seem contradictory. The model which is to be chosen depends on the problems that are to be solved. The basic criterion is that the model should produce correct (or approximate) predictions (which may be tested) or problem-solutions, and be as simple as possible. Further questions about the "Ding an Sich" or ultimate reality behind the model are meaningless.

The pragmatic epistemology does not give a clear answer to the question where knowledge or models come from. There is an implicit assumption that models are built from parts of other models and empirical data on the basis of trial-and-error complemented with some heuristics or intuition. A more radical point of departure is offered by constructivism. It assumes that all knowledge is built up from scratch by the subject of knowledge. There are no 'givens', neither objective empirical data or facts, nor inborn categories or cognitive structures. The idea of a correspondence or reflection of external reality is rejected. Because of this lacking connection between models and the things they represent, the danger with constructivism is that it may lead to relativism, to the idea that any model constructed by a subject is as good as any other and that there is no way to distinguish adequate or 'true' knowledge from inadequate or 'false' knowledge.

We can distinguish two approaches trying to avoid such an 'absolute relativism'. The first may be called individual constructivism. It assumes that an individual attempts to reach coherence among the different pieces of knowledge. Constructions that are inconsistent with the bulk of other knowledge that the individual has will tend to be rejected. Constructions that succeed in integrating previously incoherent pieces of knowledge will be maintained. The second, to be called social constructivism, sees consensus between different subjects as the ultimate criterion to judge knowledge. 'Truth' or 'reality' will be accorded only to those constructions on which most people of a social group agree.

In these philosophies, knowledge is seen as largely independent of a hypothetical 'external reality' or environment. As the 'radical' constructivists Maturana and Varela argue, the nervous system of an organism cannot in any absolute way distinguish between a perception (caused by an external phenomenon) and a hallucination (a purely internal event). The only basic criterion is that different mental entities or processes within or between individuals should reach some kind of equilibrium.

Though these constructivistic approaches put much more emphasis on the changing and relative character of knowledge, they are still absolutist in the primacy they give to either social consensus or internal coherence, and their description of construction processes is quite vague and incomplete. A more broad or synthetic outlook is offered by different forms or evolutionary epistemology. Here it is assumed that knowledge is constructed by the subject or group of subjects in order to adapt to their environment in the broad sense. That construction is an on-going process at different levels, biological as well as psychological or social. Construction happens through blind variation of existing pieces of knowledge, and the selective retention of those new combinations that somehow contribute most to the survival and reproduction of the subject(s) within their given environment. Hence we see that the 'external world' again enters the picture, although no objective reflection or correspondence is assumed, only an equilibrium between the products of internal variation and different (internal or external) selection criteria. Any form of absolutism or permanence has disappeared in this approach, but knowledge is basically still a passive instrument developed by organisms in order to help them in their quest for survival.

A most recent, and perhaps most radical approach, extends this evolutionary view in order to make knowledge actively pursue goals of its own. This approach, which as yet has not had the time to develop a proper epistemology, may be called memetics. It notes that knowledge can be transmitted from one subject to another, and thereby loses its dependence on any single individual. A piece of knowledge that can be transmitted or replicated in such a way is called a 'meme'. The death of an individual carrying a certain meme now no longer implies the elimination of that piece of knowledge, as evolutionary epistemology would assume. As long as a meme spreads more quickly to new carriers, than that its carriers die, the meme will proliferate, even though the knowledge it induces in any individual carrier may be wholly inadequate and even dangerous to survival. In this view a piece of knowledge may be succesful (in the sense that it is common or has many carriers) even though its predictions may be totally wrong, as long as it is sufficiently 'convincing' to new carriers. Here we see a picture where even the subject of knowledge has lost his primacy, and knowledge becomes a force of its own with proper goals and ways of developing itself. That this is realistic can be illustrated by the many superstitions, fads, and irrational beliefs that have spread over the globe, sometimes with a frightening speed.

Like social constructivism, memetics attracts the attention to communication and social processes in the development of knowledge, but instead of seeing knowledge as constructed by the social system, it rather sees social systems as constructed by knowledge processes. Indeed, a social group can be defined by the fact that all its members share the same meme (Heylighen, 1992). Even the concept of 'self', that which distinguishes a person as a individual, can be considered as a piece of knowledge, constructed through social processes (HarrŽ, 19), and hence a result of memetic evolution. From a constructivist approach, where knowledge is constructed by individuals or society, we have moved to a memetic approach, which sees society and even individuality as byproducts constructed by an ongoing evolution of independent fragments of knowledge competing for domination.

We have come very far indeed from Plato's immutable and absolute Ideas, residing in an abstract realm far from concrete objects or subjects, or from the naive realism of the reflection-correspondence theory, where knowledge is merely an image of external objects and their relations. At this stage, the temptation would be strong to lapse into a purely anarchistic or relativistic attitude, stating that 'anything goes', and that it would be impossible to formulate any reliable and general criteria to distinguish 'good' or adequate pieces of knowledge from bad or inadequate ones. Yet in most practical situations, our intuition does help us to distinguish perceptions from dreams or hallucinations, and unreliable predictions ('I am going to win the lottery') from reliable ones ('The sun will come up tomorrow morning'). And an evolutionary theory still assumes a natural selection which can be understood to a certain degree. Hence we may assume that it is possible to identify selection criteria, but one of the lessons of this historical overview will be that we should avoid to quickly formulate one absolute criterion. Neither correspondence, nor coherence or consensus, and not even survivability, are sufficient to ground a theory of knowledge. At this stage we can only hope to find multiple, independent, and sometimes contradictory criteria, whose judgment may quickly become obsolete. Yet if we would succeed to formulate these criteria clearly, within a simple and general conceptual framework, we would have an epistemology that synthesizes and extends al of the traditional and less traditional philosophies above.

# Metaphysics, introduction

Author: Turchin, Joslyn, Heylighen,
Updated: Aug 1993
Filename: METAPHI.html

A metalanguage is still a language, and a meta-theory a theory. Meta-mathematics is a branch of mathematics. Is metaphysics a branch of physics? "Meta" in Greek means over, and --- since when you jump over something you find yourself behind or after it --- it is also understood as behind and after. The word "metaphysics" is said to originate from the mere fact that the corresponding part of Aristotle's work was positioned right after the part called "physics". But it is not unlikely that the term won a ready acceptance as denoting this part of philosophy because it conveyed the purpose of metaphysics, which is to reach beyond nature (physis) as we perceive it, and to discover the "true nature" of things, their ultimate essence and the reason for being.

Such a theory would obviously be priceless for judging and constructing more specific physical theories. When we understand language as a hierarchical model of reality, i.e. a device which produces predictions, and not as a true static picture of the world, metaphysics is understood as much more valuable than just the "free fantasy" of philosophers. To say that the real nature of the world is a certain way means to propose the construction of a model of the world along those lines. Metaphysics creates a linguistic model (logical or conceptual structure) to serve as a basis for further refinements. Even though a mature physical theory fastidiously distinguishes itself from metaphysics by formalizing its basic notions and introducing verifiable criteria, metaphysics, in a very important sense, is physics.

Philosophies traditionally start with an ontology or metaphysics: a theory of being in itself, of the essence of things, of the fundamental principles of existence and reality. In a traditional systemic philosophy, "organization" might be seen as the fundamental principle of being, rather than God, matter, or the laws of nature. However this still begs the question of where this organization comes from. In a constructive systemic philosophy, on the other hand, the essence is the process through which this organization is created.

# Process Metaphysics

Author: F. Heylighen,
Updated: Jan 24, 1997
Filename: PROCMETA.html

Many philosophers have attempted to build a process metaphysics or an evolutionary philosophy, including Alfred North Whitehead, Teilhard de Chardin, Herbert Spencer, and Henry Bergson. Their main idea is to ground a philosophy on change or development, rather than on static concepts like matter or mind. However, these early process philosophies are characterized by vagueness and mysticism, and they tend to see evolution as teleological, goal directed, guided by some supra-physical force, rather than as the blind variation and selection process that we postulate. They are thus not constructivist in the sense discussed in section constructivism.

See further:

# Ontology, introduction

Author: F. Heylighen,
Updated: Aug 15, 1995
Filename: ONTOLI.html
1. a branch of metaphysics relating to the nature and relations of being
2. a particular theory about the nature of being or the kinds of existence
Ontology (the "science of being") is a word, like metaphysics, that is used in many different senses. It is sometimes considered to be identical to metaphysics, but we prefer to use it in a more specific sense, as that part of metaphysics that specifies the most fundamental categories of existence, the elementary substances or structures out of which the world is made. Ontology will thus analyse the most general and abstract concepts or distinctions that underlie every more specific description of any phenomenon in the world, e.g. time, space, matter, process, cause and effect, system.

Recently, the term of "(formal) ontology" has been up taken by researchers in Artificial Intelligence, who use it to designate the building blocks out of which models of the world are made.(see e.g. "What is an ontology?"). An agent (e.g. an autonomous robot) using a particular model will only be able to perceive that part of the world that his ontology is able to represent. In a sense, only the things in his ontology can exist for that agent. In that way, an ontology becomes the basic level of a knowledge representation scheme. See for example my set of link types for a semantic network representation which is based on a set of "ontological" distinctions: changing-invariant, and general-specific.

# Ethics, introduction

Updated: Aug 1993
Filename: ETHICSI.html

[Node to be completed]

# What is a world view?

Author: F. Heylighen,
Updated: Dec 9, 1996
Filename: WORLVIEW.html

One of the biggest problems of present society is the effect of overall change and acceleration on human psychology. Neither individual minds nor collective culture seem able to cope with the unpredictable change and growing complexity. Stress, uncertainty and frustration increase, minds are overloaded with information, knowledge fragments, values erode, negative developments are consistently overemphasized, while positive ones are ignored. The resulting climate is one of nihilism, anxiety and despair. While the wisdom gathered in the past has lost much of its validity, we don't have a clear vision of the future either. As a result, there does not seem to be anything left to guide our actions.

What we need is a framework that ties everything together, that allows us to understand society, the world, and our place in it, and that could help us to make the critical decisions which will shape our future. It would synthesize the wisdom gathered in the different scientific disciplines, philosophies and religions. Rather than focusing on small sections of reality, it would provide us with a picture of the whole. In particular, it would help us to understand, and therefore cope with, complexity and change. Such a conceptual framework may be called a "world view".

The Belgian philosopher Leo Apostel has devoted his life to the development of such an integrating world view. As he quickly understood, the complexity of this task is too great for one man. Therefore, a major part of Apostel's efforts were directed at gathering other people, with different scientific and cultural backgrounds, to collaborate on this task. Only in the last years of his life, after several failed attempts, did he managed to create such an organization: the "Worldviews" group, which includes people from disciplines as diverse as engineering, psychiatry, theology, theoretical physics, sociology and biology.

Their first major product was a short book entitled "World views, from fragmentation to integration". This booklet is a call to arms, a program listing objectives rather than achievements. Its main contribution is a clear definition of what a world view is, and which are its necessary components. The "Worldviews" group has continued to work on different components and aspects of this general objective. Many of its members are also involved in a new interdisciplinary research center at the Free University of Brussels, which is named after Leo Apostel: the "Center Leo Apostel".

The book lists seven fundamental components of a world view. I will discuss them one by one, using a formulation which is slightly different from the one in the book, but which captures the main ideas.

A model of the world
It should allow us to understand how the world functions and how it is structured. "World" here means the totality, everything that exists around us, including the physical universe, the Earth, life, mind, society and culture. We ourselves are an important part of that world. Therefore, a world view should also answer the basic question: "Who are we?".

Explanation
The second component is supposed to explain the first one. It should answer the questions: "Why is the world the way it is? Where does it all come from? Where do we come from?". This is perhaps the most important part of a world view. If we can explain how and why a particular phenomenon (say life or mind) has arisen, we will be able to better understand how that phenomenon functions. It will also help us to understand how that phenomenon will continue to evolve.

Futurology
This extrapolation of past evolution into the future defines a third component of a world view: futurology. It should answer the question "Where are we going to?" It should give us a list of possibilities, of more or less probable future developments. But this will confront us with a choice: which of the different alternatives should we promote and which should we avoid?

Values
This is the more fundamental issue of value: "What is good and what is evil?" The theory of values defines the fourth component of a world view. It includes morality or ethics, the system of rules which tells us how we should or should not behave. It also gives us a sense of purpose, a direction or set of goals to guide our actions. Together with the answer to the question "why?", the answer to the question "what for?", may help us to understand the real meaning of life.

Action
Knowing what to strive for does not yet mean knowing how to get there, though. The next component must be a theory of action (praxiology). It would answer the question "How should we act?" It would help us to solve practical problems and to implement plans of action.

Knowledge
Plans are based on knowledge and information, on theories and models describing the phenomena we encounter. Therefore, we need to understand how we can construct reliable models. This is the component of knowledge acquisition. It is equivalent to what in philosophy is called "epistemology" or "the theory of knowledge". It should allow us to distinguish better theories from worse theories. It should answer the traditional philosophical question "What is true and what is false?"

Building Blocks
The final point on the agenda of a world view builder is not meant to answer any fundamental question. It just reminds us that world views cannot be developed from scratch. You need building blocks to start with. These building blocks can be found in existing theories, models, concepts, guidelines and values, scattered over the different disciplines and ideologies. This defines the seventh component: fragments of world views as a starting point.

The Principia Cybernetica Project has decided to build an evolutionary-systemic world view, which starts from the different concepts and principles developed in cybernetics, systems theory and the theory of evolution. Its world view can be summarized in the form of answers to a list of eternal philosophical questions.

# Cybernetics and Systems Theory

Author: F. Heylighen,
Updated: Apr 29, 1996
Filename: CYBSYSTH.html

The following links provide general background material on the field of Cybernetics and Systems Theory. This material was collected and is provided in the context of the Principia Cybernetica Project, but can be consulted independently of the rest of the project.

Cybernetics and Systems Theory is an interdisciplinary academic domain. Although there are relatively few research centers and even fewer educational programs devoted to the domain, a lot of activity is going on in between established departments. This is shown by the number of associations, conferences and journals active in the domain.

The best way of getting acquainted with the main ideas of cybernetics and systems theory is to read a few of the classic books or papers defining the domain. Other, specific bibliographic references can be found in the library database of the Department of Medical Cybernetics and AI at the University of Vienna. There also exists more general reference material, including our own Web Dictionary of basic concepts.

You can get in touch with cybernetics and systems people via existing mailing lists and newsgroups, personal or departmental home pages, or by visiting conferences in the field (see the Calendar of events from the International Federation of Systems Research).

# What are Cybernetics and Systems Science?

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Feb 18, 1998
Filename: CYBSWHAT.html

Cybernetics and Systems Science (also: "(General) Systems Theory" or "Systems Research") constitute a somewhat fuzzily defined academic domain, that touches virtually all traditional disciplines, from mathematics, technology and biology to philosophy and the social sciences. It is more specifically related to the recently developing "sciences of complexity", including AI, neural networks, dynamical systems, chaos, and complex adaptive systems.

Systems theory or systems science argues that however complex or diverse the world that we experience, we will always find different types of organization in it, and such organization can be described by principles which are independent from the specific domain at which we are looking. Hence, if we would uncover those general laws, we would be able to analyse and solve problems in any domain, pertaining to any type of system. The systems approach distinguishes itself from the more traditional analytic approach by emphasizing the interactions and connectedness of the different components of a system.

Many of the concepts used by system scientists come from the closely related approach of cybernetics: information, control, feedback, communication... Cybernetics, deriving from the Greek word for steersman (kybernetes), was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in society and in individual human beings). It grew out of Shannon's information theory, which was designed to optimize the transmission of information through communication channels, and the feedback concept used in engineering control systems. In its present incarnation of "second-order cybernetics", its emphasis is on how observers construct models of the systems with which they interact (see constructivism).

In fact cybernetics and systems theory study essentially the same problem, that of organization independent of the substrate in which it is embodied. Insofar as it is meaningful to make a distinction between the two approaches, we might say that systems theory has focused more on the structure of systems and their models, whereas cybernetics has focused more on how systems function, that is to say how they control their actions, how they communicate with other systems or with their own components, ... Since structure and function of a system cannot be understood in separation, it is clear that cybernetics and systems theory should be viewed as two facets of a single approach.

This insight has had as a result that the two domains have in practice almost merged: many, if not most, of the central associations, journals and conferences in the field include both terms, "systems" and "cybernetics", in their title.

The following links should provide plenty of introductory material and references. An excellent, easy to read overview of the systems approach can be found in our web edition of the book "The Macroscope". Together with our dictionary, and list of basic books and papers, this should be sufficient for an introductory course in the domain:

# What is Systems Theory?

Author: F. Heylighen, C. Joslyn,
Updated: Nov. 1, 1992
Filename: SYSTHEOR.html

 Synopsys: Systems Theory: the transdisciplinary study of the abstract organization of phenomena, independent of their substance, type, or spatial or temporal scale of existence. It investigates both the principles common to all complex entities, and the (usually mathematical) models which can be used to describe them.

Systems theory was proposed in the 1940's by the biologist Ludwig von Bertalanffy (: General Systems Theory, 1968), and furthered by Ross Ashby (Introduction to Cybernetics, 1956). von Bertalanffy was both reacting agaInst reductionism and attempting to revive the unity of science. He emphasized that real systems are open to, and interact with, their environments, and that they can acquire qualitatively new properties through emergence, resulting in continual evolution. Rather than reducing an entity (e.g. the human body) to the properties of its parts or elements (e.g. organs or cells), systems theory focuses on the arrangement of and relations between the parts which connect them into a whole (cf. holism). This particular organization determines a system, which is independent of the concrete substance of the elements (e.g. particles, cells, transistors, people, etc). Thus, the same concepts and principles of organization underlie the different disciplines (physics, biology, technology, sociology, etc.), providing a basis for their unification. Systems concepts include: system-environment boundary, input, output, process, state, hierarchy, goal-directedness, and information.

The developments of systems theory are diverse (Klir, Facets of Systems Science, 1991), including conceptual foundations and philosophy (e.g. the philosophies of Bunge, Bahm and Laszlo); mathematical modeling and information theory (e.g. the work of Mesarovic and Klir); and practical applications. Mathematical systems theory arose from the development of isomorphies between the models of electrical circuits and other systems. Applications include engineering, computing, ecology, management, and family psychotherapy. Systems analysis, developed independently of systems theory, applies systems principles to aid a decisIon-maker with problems of identifying, reconstructing, optimizing, and controlling a system (usually a socio-technical organization), while taking into account multiple objectives, constraints and resources. It aims to specify possible courses of action, together with their risks, costs and benefits. Systems theory is closely connected to cybernetics, and also to system dynamics, which models changes in a network of coupled variables (e.g. the "world dynamics" models of Jay Forrester and the Club of Rome). Related ideas are used in the emerging "sciences of complexity", studying self-organization and heterogeneous networks of interacting actors, and associated domains such as far-from-equilibrium thermodynamics, chaotic dynamics, artificial life, artificial intelligence, neural networks, and computer modeling and simulation.

Francis Heylighen and Cliff Joslyn

Prepared for the Cambridge Dictionary of Philosophy.(Copyright Cambridge University Press)

# Analytic vs. Systemic Approaches

Author: J. de Rosnay
Updated: Feb 17, 1997
Filename: ANALSYST.html

The analytic and the systemic approaches are more complementary than opposed, yet neither one is reducible to the other.

The analytic approach seeks to reduce a system to its elementary elements in order to study in detail and understand the types of interaction that exist between them. By modifying one variable at a time, it tries to infer general laws that will enable one to predict the properties of a system under very different conditions. To make this prediction possible, the laws of the additivity of elementary properties must be invoked. This is the case in homogeneous systems, those composed of similar elements and having weak interactions among them. Here the laws of statistics readily apply, enabling one to understand the behavior of the multitude-of disorganized complexity.

The laws of the additivity of elementary properties do not apply in highly complex systems composed of a large diversity of elements linked together by strong interactions. These systems must be approached by new methods such as those which the systemic approach groups together. The purpose of the new methods is to consider a system in its totality, its complexity, and its own dynamics Through simulation one can "animate" a system and observe in real time the effects of the different kinds of interactions among its elements. The study of this behavior leads in time to the determination of rules that can modify the system or design other systems.

The following table compares, one by one, the traits of the two approaches.
 Analytic Approach Systemic Approach isolates, then concentrates on the elements unifies and concentrates on the interaction between elements studies the nature of interaction studies the effects of interactions emphasizes the precision of details emphasizes global perception modifies one variable at a time modifies groups of variables simultaneously remains independent of duration of time; the phenomena considered are reversible. integrates duration of time and irreversibility validates facts by means of experimental proof within the body of a theory validates facts through comparison of the behavior of the model with reality uses precise and detailed models that are less useful in actual operation (example: econometric models) uses models that are insufficiently rigorous to be used as bases of knowledge but are useful in decision and action (example: models of the Club of Rome) has an efficient approach when interactions are linear and weak has an efficient approach when interactions are nonlinear and strong leads to discipline-oriented (juxtadisciplinary) education leads to multidisciplinary education leads to action programmed in detail leads to action through objectives possesses knowledge of details poorly defined goals possesses knowledge of goals, fuzzy details

This table, while useful in its simplicity, is nevertheless a caricature of reality. The presentation is excessively dualist; it confines thought to an alternative from which it seems difficult to escape. Numerous other points of comparison deserve to be mentioned. Yet without being exhaustive the table has the advantage of effectively opposing the two complementary approaches, one of which-the analytic approach-has been favored disproportionately in our educational system.

# The Nature of Cybernetic Systems

Author: C. Joslyn,
Updated: Jan 1992
Filename: CYBSNAT.html

While as a meta-theory, the ideas and principles of Cybernetics and Systems Science are intended to be applicable to anything, the "interesting" objects of study that Cybernetics and Systems Science tends to focus on are complex systems such as organisms, ecologies, minds, societies, and machines. Cybernetics and Systems Science regards these systems as complex, multi-dimensional networks of information systems. We will generally call such systems "cybernetic systems" (see also "complex adaptive systems"). Cybernetics presumes that there are underlying principles and laws which can be used to unify the understanding of such seemingly disparate types of systems. The characteristics of cybernetic systems directly affect the nature of cybernetic theory, resulting in serious challenges to traditional methodology. Some of these characteristics are:

Complexity:
Cybernetic systems are complex structures, with many heterogeneous interacting components.
Mutuality:
These many components interact in parallel, cooperatively, and in real time, creating multiple simultaneous interactions among subsystems.
Complementarity:
These many simultaneous modes of interaction lead to subsystems which participate in multiple processes and structures, yielding any single dimension of description incomplete, and requiring multiple complementary, irreducible levels of analysis.
Evolvability:
Cybernetic systems tend to evolve and grow in an opportunistic manner, rather than be designed and planned in an optimal manner.
Constructivity:
Cybernetic systems are constructive, in that as they tend to increase in size and complexity, they become historically bound to previous states while simultaneously developing new traits.
Reflexivity:
Cybernetic systems are rich in internal and external feedback, both positive and negative. Ultimately, they can enter into the "ultimate" feedback of reflexive self-application, in which their components are operated on simultaneously from complementary perspectives, for example as entities and processes. Such situations may result in the reflexive phenomena of self-reference, self-modeling, self-production, and self-reproduction.

# Cybernetics and Systems Science in Academics

Author: C. Joslyn, F. Heylighen,
Updated: Jan 1992

The fundamental concepts of cybernetics have proven to be enormously powerful in a variety of disciplines: computer science, management, biology, sociology, thermodynamics, etc. Cybernetics and Systems Science combine the abstraction of philosophy and mathematics with the concreteness of dealing with the theory and modeling of "real world" evolving systems. Since they are inherently interdisciplinary, Cybernetics and Systems Science work between and among standard theories, usually pairwise (e.g. biophysics, sociobiology) but sometimes across more than two types of systems.

Some recent fashionable approaches have their roots in ideas that were proposed by cyberneticians many decades ago: e.g. artificial intelligence, neural networks, complex systems, human-machine interfaces, self-organization theories, systems therapy, etc. Most of the fundamental concepts and questions of these approaches have already been formulated by cyberneticians such as Wiener, Ashby, von Bertalanffy \cite{V L56}, Boulding, von Foerster, von Neumann, McCulloch, and Pask in the 1940's through 1960's.

But since its founding, Cybernetics and Systems Science have struggled to find a degree of "respectability" in the academic community. While little interdisciplinary work has prospered recently, cyberneticians especially have failed to find homes in academic institutions, or to create their own. Very few academic programs in Cybernetics and Systems Science exist, and those working in the new disciplines described above seem to have forgotten their cybernetic predecessors.

What is the reason that cybernetics does not get the popularity it deserves? What distinguishes cyberneticians from researchers in the previously mentioned areas is that the former stubbornly stick to their objective of building general, domain independent theories, whereas the latter focus on very specific applications: expert systems, psychotherapy, thermodynamics, pattern recognition, etc. General integration remains too abstract, and is not sufficiently successful to be really appreciated.

As an interdisciplinary field, Cybernetics and Systems Science sees common concepts used in multiple traditional disciplines and attempts to achieve a consensual unification by finding common terms for similar concepts in these multiple disciplines. Thus sometimes Cybernetics and Systems Science abstracts away from concepts, theories, and terminologies in specific discipline towards general, and perhaps idiosyncratic, usages. These new conceptual categories may not be recognizable to the traditional researchers, or they may find no utility in the use of the general concepts.

Clearly the problem of building a global theory is much more complex than any of the more down-to-earth goals of the fashionable approaches. But we may also say that the generality of the approach is dangerous in itself if it leads to being "stuck" in abstractions which are so far removed from the everyday world that it is difficult to use them, interact with them, or test them on concrete problems; in other words, to get a feel for how they behave and what their strengths and weaknesses are.

Although there are many exceptions, researchers in Cybernetics and Systems Science tend to be trained in a traditional specialty (like biology, management, or psychology) and then come to apply themselves to problems in other areas, perhaps a single other area. Thus their exposure to Cybernetics and Systems Science concepts and theory tends to be somewhat ad hoc and specific to the two or three fields they apply themselves to.

# Existing Cybernetic Foundations

Author: C. Joslyn,
Filename: CYBFOUND.html

While traditional disciplines develop a single consistent theory, or perhaps multiple competing, yet still internally consistent, theories within them, cybernetics and systems theory has not generally been successful at this task. The foundations of cybernetics and systems theory show a frightening lack of serious attention, and are marked by semantic squabbles, and (as a result of both ignorance and turf fighting) an inexcusable separation of "camps" from each other.

Few have even attempted to address foundational theoretical and methodological issues in anything other than an ad hoc manner. Some conceptual "frameworks" exist at the formal, mathematical level \cite{KLG85c,MEMTA88}. Some researchers have presented integrated conceptual frameworks for major areas of systems science \cite{JAE80a,ODH83,POW73,TUV77}, and there have been some attempts to develop the foundations of the philosophy underlying cybernetics and systems theory \cite{BUM74,LAE72}. Yet these works focus specifically on cybernetics and systems theory from the perspectives of the traditional fields of mathematics or philosophy respectively; they are still locked into the traditional forms of development of academic work. There is as yet no systems theory of systems theories.

There is at the same time a lack of researchers who are willing or able to address themselves to the general problems and theories encompassed by cybernetics and systems theory. The lack of a coherent terminology and methodology is reflected in a lack of basic textbooks and glossaries, (with some exceptions \cite{ASR56,KLG91a,WEG75}) and further in a failure to establish even primary educational programs to instruct upcoming generations. What little interdisciplinary work has prospered has profited from the developments in cybernetics and systems theory over the past few decades while either ignoring or deliberately avoiding any reliance on cybernetics and systems theory (e.g. cite{SFI,WOS88}).

The lack of a strong foundation for or consensus within cybernetics and systems theory extends to the very basic information about the field. How do we describe ourselves, what can we tell new students and outsiders? Cybernetics and systems theory has been alternatively described as a science, a point of view, a world-view, an approach, an outlook, or a kind of applied philosophy or applied mathematics. There are those in our community who approve of and even champion this state of affairs. They focus on the creativity of the maverick academics who are drawn to cybernetics and systems theory, and decry any attempts to structure or build a solid theory.(Again, with some notable exceptions \cite{UMS90}.) Clearly this lack of balance has led to rather poor review standards in systems journals and conferences, and a low "signal to noise ratio".

What can account for the current state of affairs in cybernetics and systems theory, the lack of a consensually held fundamental theory? Is it inherent in the field, and necessary in any broad interdisciplinary studies? Or is it an historical accident, exacerbated by the personalities and careers of individual researchers? The Principia Cybernetica Project holds that there are in fact fundamental and foundational concepts, principles, and theories immanent in the body and literature of cybernetics and systems theory which do hold to general information systems, including all living and evolving systems at all levels of analysis. We contend that the lack of a fundamental theory is due to a lack of investment in the field. Support for and investment in a field are mutually reinforcing. A lack of either will lead to a lack of the other.

# Cybernetic Technology

Author: Heylighen,
Updated: Oct 18, 1993
Filename: CYBTECH.html

Cybernetics was originally defined in 1947 by Wiener as the science of communication and control, and grew out of Shannon's information theory, which was designed to optimize the transfer of information through communication channels (e.g. telephone lines), and the feedback concept used in engineering control systems. Information and control technologies have gone a very long way since, especially through the introduction of the computer as an all-purpose information processing tool. Most of the presently most fashionable computing applications derive from ideas originally proposed by cyberneticians several decades ago: AI, neural networks, machine learning, autonomous agents, artificial life, man-machine interaction, etc.

The domain of computing applications has grown so quickly that labeling anything that uses a computer as "cybernetic" is more obscuring than enlightening. Therefore we would restrict the label "cybernetic technology" to those information processing and transmitting tools that somehow increase the general purpose "intelligence" of the user, that is to say the control the user has over information and communication.

Especially all "value-added" computer-supported communication technologies (electronic mailing list, such as PRNCYB-L, newsgroups and bulletin boards, various forms of groupware, electronic publishing tools such as FTP or WWW) fall under this heading. They make it possible to exchange information in a very fast, simple and reliable way, so that it is automatically stored and ready for immediate further processing or transfer. The practical implication is that communication channels between far-away locations becomes so flexible and direct that they remind us of nerves, connecting and controlling different parts of an organism. The group of cooperators thus can behave more like a single system, with a vastly increased knowledge and intelligence, rather than like a collection of scattered individuals who now and then exchange limited messages, that need a lot of time to reach their destination and be processed.

In addition to communication, there is the aspect of increased control over information. The is especially obvious in computing tools that offer some kind of additional intelligence to the user: 1) everything deriving from artificial intelligence, and its daughter fields, such as expert systems, machine learning, and neural networks, where certain cognitive processes are automatized and thus taken over from the user; 2) the different tools that offer better ways to organize and represent information or knowledge, i.e. that support the user in building useful models. This category includes all types of computer simulation (e.g. virtual reality), knowledge representation tools, hypertext and multimedia, databases and information retrieval. The two features of computer intelligence and modelling are merged in what may be called "knowledge structuring": the use of computer programs that reorganize models in order to make them more adequate (more correct, simple, rich, easy-to-use, ...). (see a short paper by me, suggesting a possible way to introduce knowledge structuring in hypertexts)

The merging of the twin cybernetic dimensions of communication and control leads us to envision an all-encompassing, "intelligent" communication network, cyberspace, which may form the substrate for an emerging world-wide super-brain.

# Cyberspace

Author: Heylighen,
Updated: Oct 17, 1994
Filename: CYBSPACE.html

"Cyberspace is the place where a telephone conversation appears to occur. Not inside your actual phone, the plastic device on your desk. Not inside the other person's phone, in some other city. _The_place_between_ the phones. The indefinate place _out_there_, where the two of you, human beings, actually meet and communicate."

Bruce Sterling [The Hacker Crackdown]

The word "cyberspace" was coined by the science fiction author William Gibson, when he sought a name to describe his vision of a global computer network, linking all people, machines and sources of information in the world, and through which one could move or "navigate" as through a virtual space.

The word "cyber", apparently referring to the science of cybernetics, was well-chosen for this purpose, as it derives from the Greek verb "Kubernao", which means "to steer" and which is the root of our present word "to govern". It connotes both the idea of navigation through a space of electronic data, and of control which is achieved by manipulating those data. For example, in one of his novels Gibson describes how someone, by entering cyberspace, could steer computer-controlled helicopters to a different target. Gibson's cyberspace is thus not a space of passive data, such as a library: its communication channels connect to the real world, and allow cyberspace navigators to interact with that world. The reference to cybernetics is important in a third respect: cybernetics defines itself as a science of information and communication, and cyberspace's substrate is precisely the joint network of all existing communication channels and information stores connecting people and machines.

The word "space", on the other hand, connotes several aspects. First, a space has a virtually infinite extension, including so many things that they can never be grasped all at once. This is a good description of the already existing collections of electronic data, on e.g. the Internet. Second, space connotes the idea of free movement, of being able to visit a variety of states or places. Third, a space has some kind of a geometry, implying concepts such as distance, direction and dimension.

The most direct implementation of the latter idea is the technology of virtual reality, where a continuous three-dimensional space is generated by computer, which reacts to the user's movements and manipulations like a real physical space would. In a more metaphorical way, the geometry (or at least topology) of space can be found in the network of links and references characterizing a hypertext (which can be seen as the most general form for a collection of interlinked data). Nodes in a hypertext can be close or distant, depending on the number of links one must traverse in order to get from the one to the other. Moreover, the set of links in a given node define a number of directions in which one can move. However, a hypertext does not seem to have any determined number of dimensions (except perhaps infinity), it is not continuous but "chunky", and the distance between two points is in general different depending on the point from which one starts to move.

One of the challenges for the researchers who are trying to make present computer networks look more like a Gibsonian cyberspace is to integrate the intuitive geometry of 3-D virtual reality, with the more general, but cognitively confusing, infinite dimensionality of hypertext nets (see e.g. NCSA's project on navigation through information space). A first step in that direction are the extensions to World-Wide Web which allow the user to do hypermedia navigation in a two-dimensional image (e.g. a map of Internet Resources), by associating clicks in different areas of the image with different hyperlinks. More ambitious proposals to develop a Virtual Reality interface to the World-Wide Web are being discussed.

As a description for what presently exists, the word "cyberspace" is used in a variety of significations, which each emphasize one or more of the meanings sketched above. Some use it as a synonym for virtual reality, others as a synonym for the World-Wide Web hypermedia network, or for the Internet as a whole (sometimes including the telephone, TV, and other communication networks).

None of the uses already seems to incorporate the most intrinsically cybernetic aspect of the concept: that of a shared medium through which one can exert control over one's environment. Control can apply as well to objects in cyberspace (e.g. when you alter the information in database through a Web form interface), as to objects in the real world (telepresence or teleoperation). As a first example of the control possibilities offered by the World-Wide Web, it is possible to steer a operated robot arm to do excavations. I would venture that it is that last dimension which will turn out to be the most important one in the future, as it may form the substrate for a cybernetic "superbeing" or "metabeing"...

# Cybernetic Theory and Cybernetic Practice

Author: C. Joslyn,
Updated: Jan 1992
Filename: CYBTHPRA.html

Analysis and modeling of cybernetic systems tends to be extremely computationally expensive. Even attempting to do cybernetic theory before the advent and computational technology would have been practically impossible. Therefore, just as cybernetics grew out of the earliest developments in computer science \cite{VOJ56,MCW65}, so the development of Cybernetics and Systems Science have always been tied to computer technology) and computer modeling.

It is therefore not surprising that the use of this same technology is the bedrock of practicing cyberneticians, and further holds the promise to resolve some of these conflicts between the objects and nature of cybernetic theory and the nature of academic work. In particular, it is now possible to develop representational media which share the characteristics of the systems being studied:

Complexity:
The miniaturization and speed of computer components allows the representation of models and systems of great complexity, with many interacting elements at a variety of scales.

Complementarity:
Not only automated indexing and look-up mechanisms, but especially the recent developments in hypertext and hypermedia have allowed representations of complex systems which can have

multiple orderings, and thus a nonlinear structure.

Mutuality:
There is a great deal of current research in parallel processes and cooperative work amongst researchers. Such systems allow real-time, simultaneous interaction among many agents (either programs or people). The nonlinear structure of hypermedia allows for the representations of the work of all cooperating agents.

Evolvability:
A hallmark of electronic representations is their plasticity. Dynamic memories (such as electronic RAMs) are designed for minimal time to change their state; while even more static memories (such as tape drives) are easily modified. Furthermore, the multiple orderings available through hypermedia allow for easy location of information to be changed. This results in systems which can easily be changed and modified to reflect conditions or the desires of their creators.

Constructivity:
Again, partly due to these nonlinear representations, maintaining dynamically changing representations which record and preserve the history of their development is quite feasible. Edits, updates, and general change and growth can be represented directly, and revealed or concealed as desired.

Reflexivity:
Another hallmark of computer technology is that it is fundamentally reflexive. The ability to treat a given piece of information as either an object for manipulation or as representing something is the essence of the program/data distinction which allows for programmable machines. Some computer systems (e.g. Lisp, Smalltalk, and Refal) make this reflexivity explicit, representing program as data, or a data type as a data object, yielding programming environments which are extensible. Furthermore, the mathematical bases of computational theory in Turing machines and recursive functions are also inherently reflexive. Recursiveness in formal systems is used to represent feedback in cybernetic systems.

# Cybernetics and Systems Science and Academic Work

Author: C. Joslyn,
Updated: Aug 1993
Filename: ^CYBSWORK.html

Researchers in cybernetics and systems science work in a sometimes difficult academic environment. In many ways both the subject matter and methodologies of cyberneticians are in direct conflict with the methods and products favored by the academy. The methods of traditional academic and scientific work cannot and do not reflect the properties of cybernetic systems, and thus cybernetics and systems science are in conflict with the nature of traditional academic work and development.

Traditional analytic methods tend to focus on individual, simple subsystems in isolation, while only occasionally (and frequently inaccurately) extrapolating to group traits. Temporal and physical levels of analysis are abstracted and isolated, and disciplinary divisions cut off consideration of their interaction.

This inadequacy is reflected in the actual products of academic and scientific work, the books, papers, and lectures which are the coin in trade for academic workers. Such works (like all traditional publications) have a linear structure, ranging from long treatises to collections of short paragraphs or sections (e.g. the work of Aristotle \cite{AR43} or Wittgenstein \cite{WIL58}). Various indexing and other methods are available to gain "random access" within documents. Dictionaries, encyclopedias, and other reference works partially introduce nonlinear structures through internal references (e.g. \cite{EDP67,KRK84,FLA79}). Some authors have made halting efforts in the direction of nonlinear documents \cite{MIM86}; others have used pictures and graphical notation to aid in understanding \cite{VOH81,ABRSHC85,VAF75,HAD88}. And certainly the use of formal systems (mathematics and logical notations) have given the ability to construct large, complex linguistic systems.

Nevertheless, over the years the fundamental linear textual form has been maintained. Works are produced by single or at most small groups of authors. Collaborative work among more than two people remains next to impossible. Work proceeds almost entirely in natural language. The development of large, complex systems of philosophical thought in non-formal domains has been difficult. Once published, the works sit on library shelves in mute inactivity. They are not even open to revision except through further publications and errata. The connections among and within works are revealed only through laborious reference searches and synthetic works by diligent authors. Tracing the historical development of ideas is as laborious as that of bibliographical relation. The physical form of texts required that the products of one author or the writings on one subject be physically scattered throughout a vast published literature, leading to a cacophonous din of argument and discourse.

The disciplinary divisions of academic work also place a regimented, linear, and highly specific structure to the categorization of published books and papers. Cybernetics and systems science researchers, on the other hand, typically utilize a great deal of the library shelves, including mathematics, all the traditional sciences, psychology and sociology, philosophy, linguistics, etc. In fact, ultimately there can be little doubt that cybernetics and systems science are not "academic disciplines" at all in the traditional sense of the word. As the trans- (inter-, meta-, anti-) disciplinary studies of general systems and information systems, cybernetics and systems science has long fought against the traditional disciplinary divisions of intellectual specialization.

This critique can be extended to the ultimate reflexivity of cybernetics and systems science, in which the academic milieu in which they operate is regarded as another cybernetic system, and therefore an object of study which itself should be understood through cybernetic principles.(Similarly, Turchin \cite{TUV77} describes the ultimate end of science as the reflexive study of the scientific process.)

# Relation to other disciplines

Author: F. Heylighen,
Updated: Nov 12, 1996
Filename: CYBSREL.html

Ideas related to the domain of cybernetics and systems are used in the emerging "sciences of complexity", also called "complex adaptive systems", studying self-organization and heterogeneous networks of interacting actors (e.g. the work of the Fe Institute), and associated research in the natural sciences such as far-from-equilibrium thermodynamics, catastrophe theory, chaos and dynamical systems. A third strand are different high-level computing applications such as artificial intelligence, neural networks, man-machine interaction and computer modeling and simulation.

Unfortunately, few practitioners in these recent disciplines seem to be aware that many of their concepts and methods were proposed or used by cyberneticians since many years. Subjects like complexity, self-organization, connectionism and adaptive systems have already been extensively studied in the 1940's and 1950's, by researchers like Wiener, Ashby, von Neumann and von Foerster, and in discussion forums like the famous Josiah Macy meetings on cybernetics [Heims, 1991]. Some recent popularizing books on "the sciences of complexity" (e.g. Waldrop, 1992) seem to ignore this fact, creating the false impression that work on complex adaptive systems only started in earnest with the creation of the Santa Fe Institute in the 1980's.

Reference: S. Heims. The Cybernetics Group. MIT Press, Cambridge MA, 1991.

Author: F. Heylighen,
Updated: Nov 12, 1996
Filename: CAS.html

The recently founded Santa Fe Institute is the gathering point for a new approach, which is usually presented as the study of "complex adaptive systems" (CAS). Whereas the authors in the "natural science" tradition are mostly European, while the cybernetics and systems researchers come from different continents, the CAS movement is predominantly American. Though it shares its subject, the general properties of complex systems across traditional disciplinary boundaries, with cybernetics and systems theory , the CAS approach is distinguished by the extensive use of computer simulations as a research tool, and an emphasis on systems, such as ecologies or markets, which are less integrated or "organized" than the ones, such as organisms, companies and machines, studied by the older tradition.

Two popular science books, one by the science writer Mitchell Waldrop and one by the Nobel laureate and co-founder of the Santa Fe Institute Murray Gell-Mann, offer good reviews of the main ideas underlying the CAS approach. Another Santa Fe collaborator, the systems analyst John Casti, has written several popular science books, discussing different issues in the modelling of complex systems, while integrating insights from the CAS approach with the two older traditions.

John Holland is the founder of the domain of genetic algorithms. These are parallel, computational representations of the processes of variation, recombination and selection on the basis of fitness that underly most processes of evolution and adaptation (Holland, 1992). They have been successfully applied to general problem solving, control and optimization tasks, inductive learning (classifier systems, Holland et al., 1986), and the modelling of ecological systems (the ECHO model, Holland, 1996). The biologist Stuart Kauffman has tried to understand how networks of mutually activating or inhibiting genes can give rise to the differentiation of organs and tissues during embryological development. This led him to investigate the properties of Boolean networks of different sizes and degrees of connectedness. Through a reasoning reminiscent of Ashby, he proposes that the self-organization exhibited by such networks of genes or chemical reactions is an essential factor in evolution, complementary to Darwinian selection by the environment.

Holland's and Kauffman's work, together with Dawkins' simulations of evolution and Varela's models of autopoietic systems, provide essential inspiration for the new discipline of artificial life, This approach, initiated by Chris Langton (1989, 1992), tries to develop technological systems (computer programs and autonomous robots) that exhibit lifelike properties, such as reproduction, sexuality, swarming, and co-evolution. Tom Ray's Tierra program proposes perhaps the best example of a complex, evolving ecosystem, with different species of "predators", "parasites" and "prey", that exists only in a computer.

Backed by Kauffman's work on co-evolution, Wolfram's cellular automata studies, and Bak's investigations of self-organized criticality, Langton (1990) has proposed the general thesis that complex systems emerge and maintain on the edge of chaos, the narrow domain between frozen constancy and chaotic turbulence. The "edge of chaos" idea is another step towards an elusive general definition of complexity. Another widely cited attempt at a definition in computational terms was proposed by Charles Bennett.

Another investigation which has strongly influenced the artificial life community is Robert Axelrod's game theoretic simulation of the evolution of cooperation. By letting different strategies compete in a repeated Prisoner's Dilemma game, Axelrod (1984) showed that mutually cooperating, "tit-for-tat"-like strategies tend to dominate purely selfish ones in the long run. This transition from biological evolution to social exchanges naturally leads into the modelling of economic processes (Anderson, Arrow & Pines, 1988). W. Brian Arthur has systematically investigated self-reinforcing processes in the economy, where the traditional law of decreasing returns is replaced by a law of increasing returns, leading to the path-dependence and lock-in of contingent developments. More recently (1994), he has simulated the seemingly chaotic behavior of stock exchange-like systems by programming agents that are continuously trying to guess the future behavior of the system to which they belong, and use these predictions as basis for their actions. The conclusion is that the different predictive strategies cancel each other out, so that the long term behavior of the system becomes intrinsically unpredictable. This result leads back to von Foerster's second-order cybernetics, according to which models of social systems change the very systems they intend to model.

Bibliography: see the "classic publications on complex, evolving systems".

# Self-organization and complexity in the natural sciences

Author: F. Heylighen,
Updated: Nov 12, 1996
Filename: COMPNATS.html

An important strand of work leading to the analysis of complex evolution is thermodynamics. Ilya Prigogine received the Nobel prize for his work, in collaboration with other members of the "Brussels School", showing that physical and chemical systems far from thermodynamical equilibrium tend to self-organize by exporting entropy and thus to form dissipative structures. Both his philosophical musings (Prigogine & Stengers, 1984) about the new world view implied by self-organization and irreversible change, and his scientific work (Nicolis & Prigogine, 1977, 1989; Prigogine, 1980) on bifurcations and order through fluctuations remain classics, cited in the most diverse contexts. Inspired by Prigogine's theories, Erich Jantsch has made an ambitious attempt to synthesize everything that was known at the time (1979) about self-organizing processes, from the Big Bang to the evolution of society, into an encompassing world view.

The physicist Hermann Haken (1978) has suggested the label of synergetics for the field that studies the collective patterns emerging from many interacting components, as they are found in chemical reactions, crystal formations or lasers. Another Nobel laureate, Manfred Eigen (1992), has focused on the origin of life, the domain where chemical self-organization and biological evolution meet. He has introduced the concepts of hypercycle, an autocatalytic cycle of chemical reactions containing other cycles, and of quasispecies, the fuzzy distribution of genotypes characterizing a population of quickly mutating organisms or molecules (1979).

The modelling of non-linear systems in physics has led to the concept of chaos, a deterministic process characterized by extreme sensitivity to its initial conditions (Crutchfield, Farmer, Packard & Shaw, 1986). Although chaotic dynamics is not strictly a form of evolution, it is an important aspect of the behavior of complex systems. The science journalist James Gleick has written a popular history of, and introduction to, the field. Cellular automata, mathematical models of distributed dynamical processes characterized by a discrete space and time, have been widely used to study phenomena such as chaos, attractors and the analogy between dynamics and computation through computer simulation. Stephen Wolfram has made a fundamental classification of their types of behavior. Catastrophe theory proposes a mathematical classification of the critical behavior of continuous mappings. It was developed by René Thom (1975) in order to model the (continuous) development of (discontinuous) forms in organisms, thus extending the much older work by the biologist D' Arcy Thompson (1917).

Another French mathematician, Benoit Mandelbrot (1983), has founded the field of fractal geometry, which models the recurrence of similar patterns at different scales which characterizes most natural systems. Such self-similar structures exhibit power laws, like the famous Zipf's law governing the frequency of words. By studying processes such as avalanches and earthquakes, Per Bak (1988, 1991) has shown that many complex systems will spontaneously evolve to the critical edge between order (stability) and chaos, where the size of disturbances obeys a power law, large disturbances being less frequent than small ones. This phenomenon, which he called self-organized criticality, may also provide an explanation for the punctuated equilibrium dynamics seen in biological evolution.

Bibliography: see the "classic publications on complex, evolving systems".

# History of Cybernetics and Systems Science

Author: J. de Rosnay
Updated: Nov 6, 1996
Filename: CYBSHIST.html

Perhaps one of the best ways of seeing the strength and the impact of the systemic approach is to follow its birth and development in the lives of men and institutions.

## The Search for New Tools

We need new tools with which to approach organized complexity, interdependence, and regulation. These tools emerged in the United States in the 1940s from the cross-fertilisation of ideas that is common in the melting pot of the large universities.

In illustrating a new current of thought, it is often useful to follow a thread. Our thread will be the Massachusetts Institute of Technology (MIT). In three steps, each of about ten years, MIT was to go from the birth of cybernetics to the most critical issue, the debate on limits to growth. Each of these advances was marked by many travels back and forth--typical of the systemic approach--between machine, man, and society. In the course of this circulation of ideas there occurred transfers of method and terminology that later fertilized unexplored territory.

In the forties the first step forward led from the machine to the living organism, transferring from one to the other the ideas of feedback and finality and opening the way for automation and computers. In the fifties it was the return from the living organism to the machine with the emergence of the important concepts of memory and pattern recognition, of adaptive phenomena and learning, and new advances in bionics (Bionics attempts to build electronic machines that imitate the functions of certain organs of living beings.): artificial intelligence and industrial robots. There was also a return from the machine to the living organism, which accelerated progress in neurology, perception, the mechanisms of vision In the sixties MIT saw the extension of cybernetics and system theory to industry, society, and ecology.

Three men can be regarded as the pioneers of these great breakthroughs: the mathematician Norbert Wiener, who died in 1964, the neurophysiologist Warren McCulloch, who died in 1969; and Jay Forrester, professor at the Sloan School of Management at MIT. There are of course other men, other research teams, other universities--in the United States as well as in the rest of the world--that have contributed to the advance of cybernetics and system theory. I will mention them whenever their course of research blends with that of the MIT teams.

## "Intelligent" Machines

Norbert Wiener had been teaching mathematics at MIT since 1919. Soon after his arrival there he had become acquainted with the neurophysiologist Arturo Rosenblueth, onetime collaborator of Walter B. Cannon (who gave homeostasis its name) and now at Harvard Medical School. Out of this new friendship would be born, twenty years later, cybernetics. With Wiener's help Rosenblueth set up small interdisciplinary teams to explore the no man's land between the established sciences.

In 1940 Wiener worked with a young engineer, Julian H. Bigelow, to develop automatic range finders for antiaircraft guns. Such servomechanisms are able to predict the trajectory of an airplane by taking into account the elements of past trajectories. During the course of their work Wiener and Bigelow were struck by two astonishing facts: the seem.ingly "intelligent" behavior of these machines and the "diseases" that could affect them. Theirs appeared to be "intelligent" behavior because they dealt with "experience" (the recording of past events) and predictions of the future. There was also a strange defect in performance: if one tried to reduce the friction, the system entered into a series of uncontrollable oscillations.

Impressed by this disease of the machine, Wiener asked Rosenblueth whether such behavior was found in man. The response was affirmative: in the event of certain injuries to the cerebellum, the patient cannot lift a glass of water to his mouth; the movements are amplified until the contents of the glass spill on the ground. From this Wiener inferred that in order to control a finalized action (an action with a purpose) the circulation of information needed for control must form "a closed loop allowing the evaluation of the effects of one's actions and the adaptation of future conduct based on past performances." This is typical of the guidance system of the antiaircraft gun, and it is equally characteristic of the nervous system when it orders the muscles to make a movement whose effects are then detected by the senses and fed back to the brain.

Thus Wiener and Bigelow discovered the closed loop of information necessary to correct any action--the negative feedback loop--and they generalised this discovery in terms of the human organism.

During this period the multidisciplinary teams of Rosenblueth were being formed and organized. Their purpose was to approach the study of living organisms from the viewpoint of a servomechanisms engineer and, conversely, to consider servomechanisms with the experience of the physiologist. An early seminar at the Institute for Advanced Study at Princeton in 1942 brought together mathematicians, physiologists, and mechanical and electrical engineers. In light of its success, a series of ten seminars was arranged by the Josiah Macy Foundation. One man working with Rosenblueth in getting these seminars under way was the neurophysiologist Warren McCulloch, who was to play a considerable role in the new field of cybernetics. In 1948 two basic publications marked an epoch already fertile with new ideas: Norbert Wiener's Cybernetics, or Control and Communication in the Animal and the Machine, and The Mathematical Theory of Communication by Claude Shannon and Warren Weaver. The latter work founded information theory.

The ideas of Wiener, Bigelow, and Rosenblueth caught fire like a trail of powder. Other groups were formed in the United States and around the world, notably the Society for General Systems Research whose publications deal with disciplines far removed from engineering such as sociology, political science, and psychiatry.

The seminars of the Josiah Macy Foundation continued, opening to new disciplines: anthropology with Margaret Mead, economics with Oskar Morgenstern. Mead urged Wiener to extend his ideas to society as a whole. Above all, the period was marked by the profound influence of Warren McCulloch, director of the Neuropsychiatric Institute at the University of Illinois.

At the conclusion of the work of his group on the organization of the cortex of the brain, and especially after his discussions with Walter Pitts, a brilliant, twenty-two-year-old mathematician, McCulloch understood that a beginning of the comprehension of cerebral mechanisms (and their simulation by machines) could come about only through the cooperation of many disciplines. McCulloch himself moved from neurophysiology to mathematics, from mathematics to engineering.

Walter Pitts became one of Wiener's disciples and contributed to the exchange of ideas between Wiener and McCulloch; it was he who succeeded in convincing McCulloch to install himself at MIT in 1952 with his entire team of physiologists.

## From Cybernetics to System Dynamics

In this famous melting pot, ideas boiled. From one research group to another the vocabularies of engineering and physiology were used interchangeably. Little by little the basics of a common language of cybernetics was created: learning, regulation, adaptation, self-organization, perception, memory. Influenced by the ideas of Bigelow, McCulloch developed an artificial retina in collaboration with Louis Sutro of the laboratory of instrumentation at MIT. The theoretical basis was provided by his research on the eye of the frog, performed in 1959 in collaboration with Lettvin, Maturana, and Pitts. The need to make machines imitate certain functions typical of living organisms contributed to the speeding up of progress in the understanding of cerebral mechanisms. This was the beginning of bionics and the research on artificial intelligence and robots.

Paralleling the work of the teams of Wiener and McCulloch at MIT, another group tried to utilize cybernetics on a wider scope. This was the Society for General Systems Research, created in 1954 and led by the biologist Ludwig von Bertalanffy. Many researchers were to join him: the mathematician A. Rapoport, the biologist W. Ross Ashby, the biophysicist N. Rashevsky, the economist K. Boulding. IIn 1954 the General Systems Yearbooks began to appear; their influence was to be profound on all those who sought to expand the cybernetic approach to social systems and the industrial firm in particular.

During the fifties a tool was developed and perfected that would permit organized complexity to be approached from a totally new angle--the computer. The first ones were ENIAC (1946) and EDVAC or EDSAC (1947). One of the fastest was Whirlwind 11, constructed at MIT in 1951. It used--for the first time--a superfast magnetic memory invented by a young electronics engineer from the servomechanisms laboratory, Jay W. Forrester.

As head of the Lincoln Laboratory, Forrester was assigned by the Air Force in 1952 to coordinate the implementation of an alert and defense system, the SAGE system, using radar and computers for the first time. Its mission was to detect and prevent possible attack on American territory by enemy rockets. Forrester realized the importance of the systemic approach in the conception and control of complex organizations involving men and machines in "real time": the machines had to be capable of making vital decisions as the information arrived.

In 1961, having become a professor at the Sloan School of Management at MIT, Forrester created Industrial Dynamics. His object was to regard all industries as cybernetics systems in order to simulate and to try to predict their behavior.

In 1964, confronted with the problems of the growth and decay of cities, he extended the industrial dynamics concept to urban systems (Urban Dynamics). Finally, in 1971, he generalized his earlier works by creating a new discipline, system dynamics, and published World Dynamics. This book was the basis of the work of Dennis H. Meadows and his team on the limits to growth. Financed by the Club of Rome these works were to have worldwide impact under the name MIT Report

## History of the word "cybernetics"

Cybernetics is the discipline that studies communication and control in living beings and the machines built by man. A more philosophical definition, suggested by Louis Couffignal in 1958, considers cybernetics as "the art of assuring efficiency of action. " The word cybernetics was reinvented by Norbert Wiener in 1948 from the Greek kubernetes, pilot, or rudder. The word was first used by Plato in the sense of "the art of steering" or "the art of government ". Ampère used the word cybernetics to denote "the study of ways of governing." One of the very first cybernetics mechanisms to control the speed of the steam engine, invented by James Watt and Matthew Boulton in 1788, was called a governor, or a ball regulator. Cybernetics has in fact the same root as government: the art of managing and directing highly complex systems.

See also: the origin of cybernetics and the biographies of the most important cybernetic thinkers at the cybernetics page of the ASC

# Cybernetics and Systems Thinkers

Author: F. Heylighen,
Updated: Jan 14, 1998
Filename: CSTHINK.html

The following is a list of the most influential theorists in the field of cybernetics and systems theory, with links to their biographies, info about their work or their home page (for those that they are still alive). Their most important publications can be found in our list of basic books and papers on the domain. The role some of them played in the development of the field is discussed in our history of cybernetics and systems.

This list was provided as a special service to our readers, since we noticed that the names of these people were among the most common strings entered in our search engine. Therefore, the list is directly searchable through the PCP title search. The [Search PCP] link after each name will find all references to the name in other Principia Cybernetica Web pages, while [find books] will give you a list of books by or on the author, available through the Amazon web bookshop.

W. Ross Ashby
psychiatrist; one of the founding fathers of cybernetics; developed homeostat, law of requisite variety, principle of self-organization, and law of regulating models. Further info: ASC biography - Shalizi's notes - [Search PCP]- [Find Books]

Henri Atlan
studied self-organization in networks and cells. Further info: home page - biography (French) - [Search PCP]- [Find Books]

Gregory Bateson
anthropologist; developed double bind theory, and looked at parallels between mind and natural evolution. Further info: biography - ASC biography - the Tangled Web - Ecology of Mind page - [Search PCP] - [Find Books]

Stafford Beer
management cyberneticist; creator of the Viable System Model (VSM). Further info: ASC biography - ISSS primer - ISSS luminaries - Team Syntegrity biography - [Search PCP]- [Find Books]

Kenneth E. Boulding
economist; one of the founding fathers of general system theory. Further info: ASC biography - ideas and works - dedication - [Search PCP] - [Find Books]

Peter Checkland
creator of soft systems methodology. Further info: home page - Profile - Soft Systems Methodology - [Search PCP] - [Find Books]

Jay Forrester
engineer; creator of system dynamics, applications to the modelling of industry development, cities and the world. Further info: Home page - ASC biography - short bio - [Search PCP] - [Find Books]

George Klir
mathematical systems theorist; creator of the General Systems Problem Solver methodology for modelling. Further info: home page - [Search PCP] - [Find Books]

Niklas Luhmann
sociologist; applied theory of autopoiesis to social systems. Further info: bibliography - [Search PCP] - [Find Books]

Humberto Maturana
biologist; creator together with F. Varela of the theory of autopoiesis. Further info: ASC biography - photo - contribution to psychology and complexity theory - biology of cognition - Ecology of Mind page - short bio - The Observer Web: autopoiesis theory - [Search PCP] - [Find Books]

Warren McCulloch
neurophysiologist; first to develop mathematical models of neural networks. Further info: ASC biography - McCulloch and Pitts neurons - von Foerster's tribute - [Search PCP] - [Find Books]

James Grier Miller
biologist, creator of Living Systems Theory (LST). Further info: living systems theory - intro to Miller's LST - Miller on "The Earth as a System" - Applications of LST - [Search PCP] - [Find Books]

Edgar Morin
sociologist, developed a general transdisplinary "method": Further info: biography - summary - interview - bibliography - [Search PCP] - [Find Books]

Howard T. Odum
creator of systems ecology: Further info: biography - [Search PCP] - [Find Books]

creator of conversation theory: second order cybernetic concepts and applications to education. Further info: Pangaro's archive - In Memoriam - ISSS luminaries - ASC biography - [Search PCP] - [Find Books]

Howard Pattee
theoretical biologist; studied hierarchy and semantic closure in organisms. Further info: home page - [Search PCP] - [Find Books]

William T. Powers
engineer; creator of perceptual control theory. Further info: home page - introduction to perceptual control theory - definition of control - [Search PCP] - [Find Books]

Robert Rosen
theoretical biologist; first studied anticipatory systems, proposed category theoretic, non-mechanistic model of living systems. Further info: bibliography - [Search PCP] - [Find Books]

Claude Shannon
founder of information theory. Further info: biography - biography 2 - History of mathematics biography - biography4 - a personal biography - biography and achievements - Shannon's information theory - photos - [Search PCP] - [Find Books]

Francisco Varela
biologist; creator, together with H. Maturana of the theory of autopoiesis. Further info: biography - The Observer Web: autopoiesis theory - [Search PCP] - [Find Books]

Ludwig von Bertalanffy
biologist; founder of General System Theory. Further info: biography - [Search PCP] - [Find Books]

Ernst von Glasersfeld
psychologist; proponent of radical constructivism. Further info: biography & contact info - [Search PCP] - [Find Books]

Heinz von Foerster
one of the founding fathers of cybernetics; first to study self-organization, self-reference and other circularities; creator of second-order cybernetics. Further info: overview - biographical interview - Varela's personal introduction - [Search PCP] - [Find Books]

John von Neumann
mathematician; founding father in the domains of ergodic theory, game theory, quantum logic, axioms of quantum mechanics, the digital computer, cellular automata and self-reproducing systems. Further info: biography - bio with bibliography -History of mathematics biography - biography3 - biography4 - [Search PCP] - [Find Books]

Paul Watzlawick
psychiatrist; studied role of paradoxes in communication. Further info: ASC biography - [Search PCP] - [Find Books]

Norbert Wiener
mathematician; founder of cybernetics. Further info: ideas - biography - Shalizi's notes - Notices of the AMS bio - bio (mathematicians) - MathematicalWork - his Cybernetic Delirium - his activism - in K. Kelly's "Out of Control" - memoir - [Search PCP] - [Find Books]

# "The Macroscope", a book on the systems approach

Author: F. Heylighen,
Updated: Feb 26, 1997
Filename: MACRBOOK.html

Principia Cybernetica Web now offers the complete text and drawings of the book "The Macroscope" by Joël de Rosnay. It was originally published in 1979 by Harper & Row, (New York), but is now out of print. Therefore, we have made it again available on the web.

Dr. Joël de Rosnay, a molecular biologist, systems theorist, science writer, and futurologist, is presently Director of Strategy of the Cite des Sciences et de l'Industrie at La Villette (near Paris). He is an associate of the Principia Cybernetica Project.

This book is an excellent, easy to read introduction to cybernetics and systems thinking, with applications to living organisms, the economy and the world as a whole. The main theme is that the complex systems which govern our life should be looked at as a whole, rather than be taken apart into their constituents. The different systems, processes and mechanisms are beautifully illustrated with examples and pictures. Although the text is over 20 years old, this visionary document is still highly relevant to our present situation and state of knowledge. It is particularly recommended to people who wish to get an understanding of the basic concepts and applications of systems theory and cybernetics. The chapters below can be read independently of each other.

• Introduction: The Macroscope
• One. Through the Macroscope
• 1. Ecology (The Economics of Nature: Production, Consumption, and Decomposition; Regulation and Maintenance of Equilibriums)
• 2. The Economy (A Short History of the Economy; The Economic Machine; Recession and Inflation)
• 3. The City
• 5. The Living Organism
• 6. The Cell (Linking the Cell and the Body; The Work of the Enzymes)
• Two. The Systemic Revolution: A New Culture
• 1. History of a Global Approach (The Systemic Approach; The Search for New Tools; "Intelligent" Machines; From Cybernetics to System Dynamics)
• 2 What Is a System? (Open Systems and Complexity; Structural and Functional Aspects of Systems)
• 3. System Dynamics: The Internal Causes (Positive and Negative Feedback; Flows and Reservoirs)
• 4. Applications of the Systemic Approach (Analysis and Synthesis, Models and Simulation; The Dynamics of Maintenance and Change, The "Ten Commandments" of the Systemic Approach; Avoiding the Dangers of the Systemic Approach)
• Three. Energy and Survival
• 1. The Domestication of Energy
• 2 The Great Laws of Energy (Entropy and the Science of Heat; Energy and Power)
• 3. Metabolism and Waste in the Social Organism
• 4. Economics and Ecology (Universal Currency: The Kilocalorie; Energy Analysis; Energy Analysis and Food Production; The Competition Between Energy and Work)
• 5. Birth of the Bioindustry (New Jobs for Microbes; The Domestication of Enzymes; Controlling Fermentation and Photosynthesis; Ecoengineering)
• Four. Information and the Interactive Society
• 1. Supports of Communication (Measuring Information; Information and Entropy; The History of Communications; Descending and Ascending Information)
• 2. The New Interactive Networks (Communications Hardware; Services in Real Time; Social Impact of Services in Real Time)
• 3. Social Feedback (The Imbalance in Communications; The Media and electronic Participation; Problems of Representation; Advantages and Dangers of Society in Real Time)
• Five. Time and Evolution
• 1. Knowledge of Time (Time in the Evolution of Thought; Time in Contemporary Theories)
• 2. The Prison of Time (The Link Between Chronology and Causality; Irreducible Points of View; The Causal Explanation: Divergence; The Final Explanation: Convergence; Complementarity: A Third Route)
• 3. Evolution: Genesis of the Improbable (The Genesis of Form; Exclusion and Divergence; Equilibrium and Zero Growth; The Conquest of Time)
• Six. Values and Education
• 1. Birth of a Global Vision
• 2. The Emergence of New Values (Criticism of Authority; Criticism of Work; Criticism of Reason; Criticism of Human Relationships; Criticism of the Plan for Society)
• 3. Systemic Education (The Illusions of Educational Technology; The Basis of Systemic Education; The Principles of Systemic Education; The Methods of Systemic Education; Possible Structures of Parallel Education)
• Seven. Scenario for a World

Filename: ASC/INDEXASC.html

# Basic Books on Cybernetics and Systems Science

Author: C. Joslyn,
Updated: Jul 10, 1996
Filename: CSBOOKS.html

The following is a list of references used for the course SS-501, INTRODUCTION TO SYSTEMS SCIENCE, at the Systems Science Department of SUNY Binghamton in 1990.

Other, specific bibliographic references of books and a selected number of papers can be found in the library database of the Department of Medical Cybernetics and AI at the University of Vienna. A number of more recent books and papers can be found in our bibliography on the complex, evolving systems.

The books with links below can be securely ordered and paid for over the web from Amazon.com, the largest bookstore on the Net.

Key: ** Required
* Recommended

Abraham, Ralph, and Shaw, Chris: (1985) Dynamics: the Geometry of Behavior, v. I-III, Ariel Press
Excellent graphical introduction to dynamic systems theory.

Ackoff, Russel: (1972) On Purposeful Systems, Aldine Press, Chicago

Grand philosophy of human systems as teleological, goal-seeking. Structure, function, and purpose. Cognitive models and action in psychology; linguistics and semantics; conflict and cooperation; social systems.

Alan, TFH, and Starr, TB: (1982) Hierarchy: Perspective for Explaining Ecological Complexity, U. Chicago, Chicago

Anderson, PW, and Arrow, KJ et. al.: eds. (1988) Economy as an Evolving, Complex System, Addison-Wesley, New York

Critical anthology of system economic theory: applied mathematical techniques, dynamical theory, bounded rationality. Kauffman on "web searching"; Holland; Ruelle on nonlinear dynamics; Baum on neural nets.

Angyal, A: (1969) Logic of Systems, Penguin

Arbib, Michael A: (1972) Metaphorical Brain, Wiley, New York,

* Ashby, Ross: (1952) Design for a Brain, Wiley, New York.

A classic book, introducing fundamental systems concepts with examples related to the brain.

** (1956) Introduction to Cybernetics, Methuen, London

** (1981) Mechanisms of Intelligence: Writings of Ross Ashby/, ..... ed. Roger Conant

Atkin, RH: (1976) Mathematical Structure in Human Affairs, Heineman, London

Introduces Q-analysis, a methodology for identifying structures in data. The methodology uses some ideas of differential geometry.

Auger, Peter: (1990) Dynamics and Thermodynamics in Hier. Organized Sys., to appear

Aulin, AV: (1989) Foundations of Mathematical System Dynamics, Pergamon, Oxford

Causal recursion and its application to social science and economics, fundamental dynamics, self-steering, self-regulation, origins of life and mind.

* Aulin, AY: (1982) Cybernetic Laws of Social Progress, Pergamon, Oxford

Cybernetic social theory, including the Law of Requisite Hierarchy.

..... (1989) Foundations of Mathematical Systems Dynamics, Pergamon Press, Oxford

Barnsley, MF: (1988) Fractals Everywhere, Academic Press, San Diego

Best text on fractal geometry.

** Bateson, Gregory: (1972) Steps to an Ecology of Mind, Ballantine, New York

Bateson's critical essays. For purchase.

..... (1979) Mind and Nature, Bantam, New York

Unlike _Steps to an Ecology of Mind_, _Mind and Nature_ is an attempt at a coherent, popular statement of Bateson's philosophy.

Bayraktar, BA, and et. al., : eds. (1979) Education in Systems Science, Taylor and Francis, London

* Beer, Stafford: (1975) Platform for Change, Wiley, London

Foundational work in management cybernetics.

Bellman, Richard: (1972) Adaptive Control Processes: A Guided Tour, Princeton U, Princeton

An excellent book covering fundamental concept of systems science.

Beltrami, Edward: (1987) Mathematics for Dynamic Modeling, Academic Press, Orlando

Excellent mathematical introduction to dynamic systems theory, including catastrophe theory. Key results and theorems, examples. Many typos.

Blalock, HM: (1969) Systems Theory: From Verbal to Mathematical Formulation, Prentice Hall, Eng.Cliffs NJ

* Blauberg, IV, and Sadovsky, VN: (1977) Systems Theory: Philosophy and Methodological Problems, Progress, Moscow

One of the best overviews of philosphical and methodological development in systems theory, both in the Soviet Union and in the West.

Bogdanov, A.: (1980) Essays in Tektology, Intersystems

Translation of historical foundation of systems science.

Booth, TL: (1967) Sequential Machines and Automata Theory, Wiley, New York

One of the most comprehensive books on finite state machines, both deterministic and probablistic.

* Boulding, Ken: (1978) Ecodynamics, Sage, Beverly Hills

Unified theory of economics and social systems theory in terms of communicative processes.

..... (1985) World as a Total System, Sage, Beverley Hills

Brillouin, Leon: (1964) Scientific Uncertainty and Information, Academic Press, New York

Classic work on the relation between thermodynamics, information theory, and the necessary conditions for observability.

Brooks, DR, and Wiley, EO: (1988) Evolution as Entropy, 2nd edition, U. of Chicago, Chicago

Recent treatise on entropy as a general measure for biological study. Definitions of non-thermodynamic, non-informational entropies at multiple levels of analysis. Severely criticized.

Brown, G. Spencer: (1972) Laws of Form, Julian Press, New York

Philosophy of and notational system for propositional logic.

Basis for a whole school of graphical approaches to classical logic.

Brunner, RD, and Brewer, GD: (1971) Organized Complexity, Free Press, New York

Buckley, W: ed. (1968) Modern Systems Research for the Behavioral Scientist, Aldine, Chicago

Bunge, Mario: Method, Model, and Matter, D. Reydel

Campbell, Jeremy: (1982) Grammatical Man, Simon and Schuster, New York

Popular treatment of many aspects of cognitive science, information theory, and linguistics.

Cariani, Peter A: (1989) On the Design of Devices w/Emergent Semantic Functions, SUNY-Binghamton, Binghamton NY, NOTE: PhD Dissertation

Casti, John: (1979) Connectivity, Complexity and Catastrophe in Large-Scale Systems, J. Wiley, New York

..... * (1989) Alternate Realities: Mathematical Models of Nature and Man, Wiley, New York

Modern and very comprehensive text on mathematical modeling.

* Cavallo, Roger E: (1979) Role of Systems Methodology in Social Science Research, Martinus Nijhoff, Boston

Introduces the GSPS framework and discusses how it can be utilizes in social science research.

* Checkland, Peter: (1981) Systems Thinking, Systems Practice, Wiley, New York

Foundations of an area called soft systems methodology, for social systems management.

Christensen, Ronald: (1980) Entropy Minimax Sourcebook, Entropy Limited, Lincoln, MA, NOTE: Four volumes

..... (1983) Multivariate Statistical Modeling, Entropy Limited, Lincoln MA

Churchman, CW: (1968) Systems Approach, Delta, New York

General introduction to systems thinking in management.

..... (1971) Design of Inquiring Systems, Basic Books, New York

..... (1979) Systems Approach and its Enemies, Basic Books, New York,

Social systems philosophy. But also really about logic and mathematical description, excluded middles as "enemies"; relation of epistemics to action. Lucid, entertaining, critical.

Clemson, Barry: (1984) Cybernetics: A New Management Tool, Abacus Press, Kent

Guide to the theory and practice of management cybernetics. Based on Beer.

Codd, EF: (1968) Cellular Automata, Academic Press, New York,

On universal evolution. Ambitious, non-technical discussion.

Davies, Paul: (1988) Cosmic Blueprint, Simon and Schuster, New York

Excellent popular survey of complex systems theory.

De Chardin, Teilhard: (1959) The Phenomenon of Man, Harper and Row, New York

Early systemic evolutionary theology.

Denbigh, Kenneth G: (1975) An Inventive Universe, Hutchinson, London

On emergence and thermodynamics.

Denbigh, Kenneth G, and Denbigh, JS: (1985) Entropy in Relation to Incomplete Knowledge, Cambridge U., Cambridge

Good survey of quantum statistical dynamics, objectivity and subjecticity, basis of the fundamental assumption of thermodynamics, resolution of Gibbs paradox, relation to information theory.

Distefano, JJ, and et. al., : (1967) Feedback and Control Systems, Schaum, New York

Dretske, Fred: (1982) Knowledge and the Flow of Information, MIT Press, Cambridge

Treatise on information theory, syntax, and semantic.

Edelman, G: (1987) Neural Darwinism, Basic Books, New York

Theory of selectional processes at the neural level.

Eigen, M, and Schuster, P: (1979) The Hypercycle, Springer-Verlag, Heidelberg

Now classic work on the autocatalysis in chemical cycles: the cybernetic basis of metabolism.

Eigen, M, and R. Oswatitsch (1996): Steps Toward Life: a perspective on evolution

Erickson, Gary J: ed. (1988) Maximum-Entropy and Bayesian Methods in Science and Engineering, v. 1,2, Kluwer

Proceedings of the 5th, 6th, and 7th MaxEnt workshops. Foundations and applications. Spectral analysis, inductive reasoning, uncertainty and measurement, information theory in biology, etc.

Farlow, SJ: (1984) Self-Organizing Methods in Modeling, Marcel Dekker, New York

Feistel, Rainer, and Ebeling, Werner: (1988) Evolution of Complex Systems, Kluwer, New York

Oscillation and chaos in mechanical, electrical, chemical, and biological systems. Thermodynamics and spatial structures. Sequences, information, and language. Self-reproducgin systems, Lotka-Volterra systems.

Forrester, JW: (1961) Industrial Dynamics, MIT Press, Cambridge

..... (1971) World Dynamics, Wright and Allen, Cambridge

Influential early attempt at modeling the "world problem": the global economic-ecological web. Like the 's _Limits to Growth_.

..... * ed. (1975) Collected Papers of Jay W. Forrester, Wright-Allen, Cambridge

Papers by the outher of the "DYNAMO" differential systems tool, used for global ecological modeling.

Garey, MR, and Johnson, DS: (1979) Computers and Intractability: Guide to NP-Completeness, WH Freeman, San Francisco

One of the best monographs on computational complexity, NP-completeness and hardness, etc.

Gatlin, L: (1972) Information Theory and the Living System, Columbia U., New York

Classic work on the use of information theory in the analysis of genetic structure, evolution, and general biology.

* Gleick, James: (1987) Chaos: Making of a New Science, Viking, New York

Solid popular introduction to chaotic dynamics and fractal theory.

Glushkov, VM: (1966) Introduction to Cybernetics, Academic Press, New York

Excellent book on cybernetics, translated from Russian.

Greeniewski, H: Cybernetics Without Mathematics, Pergamon, Oxford

Gukhman, AA: (1965) Introduction to the Theory of Similarity, Acadenmic Press, New York

One of the excellent books on the theory of similarity.

* Haken, Herman: (1978) Synergetics, Springer-Verlag, Heidelberg

Original work by this unique developer of a "competitor" to systems science as the study of natural complex systems.

..... (1988) Information and Self-Organization, Springer-Verlag, New York

On synergetics as the science of complex systems. Integrates information theory, bifurcation theory, maximum entropy theory, and semantics.

Hall, AD: (1989) Metasystems Methodology, Pergamon, Oxford

Halme, A, and et. al., : eds. (1979) Topics in Systems Theory, Acta Polytechnica, Scandanavia

Hammer, PC: ed. (1969) Advances in Mathematical Systems Theory, Penn St. U, U. Park, PA

Hanken, AFG, and Reuver, HA: (1981) Social Systems and Learning Systems, Martinus Nijhoff, Boston

Happ, HH: ed. (1973) Gabriel Kron and Systems Theory, Union College Press, Schenectady NY

Hartnett, WE: ed. (1977) Systems: Approaches, Theories, Applications, Reidel, Boston

Herman, GT, and Rozenberg, G: (1975) Developmental Systems and Languages, North-Holland, New York

* Holland, John: (1976) Adaptation in Natural and Artificial Systems, U. Michigan, Ann Arbor

On the genetic algorithms method of modeling adaptive systems.

Kanerva, Penti: (1988) Sparse Distributed Memory, MIT Press, Cambridge

On the geometry of high dimensional, low cardinality spaces; application to associative memory.

Klir, George: (1969) An Approach to General Systems Theory, van Nostrand, New York

An early book that describes the nucleus of what is known now as the General Systems Problem Solver.

..... ed. (1972) Trends in General Systems Theory, Wiley, New York

Contains overviews of systems conceptual frameworks of Mesarovic, Wymore, and Klir; and other papers on some fundamental issues of systems science.

..... ed. (1981) Special Issue on Reconstructibility Analysis, in: Int. J. Gen. Sys., v. 7:1, pp. 1-107

Broekstra, Cavallo, Conant, Klir, Krippendorff

..... (1985) Architecture of Systems Problem Solving, Plenum, New York

Vast, general theory of epistemological systems, outline of a platform for general systems modeling and inductive inference.

..... **(1992) Facets of Systems Science, Plenum, New York

Reprints of most classical papers in systems science with an up-to-date introduction. Recommended for everyone as a general introduction to the domain

Klir, George, and Folger, Tina: (1987) Fuzzy Sets, Uncertainty, and Information, Prentice Hall

Primary text on fuzzy systems theory and extended information theory.

Koestler, Arthur, and Smythes, J.R.: eds. (1968) Beyond Reductionism, Hutchinson, London

Classical anthology on holism and reductionism.

Krinsky, VI: ed. (1984) Self-Organization: Autowaves and Structures Far From Equilibrium, Springer-Verlag, New York

Langton, Chris: ed. (1988) Artificial Life, Addison-Wesley

Proceedings from first artificial life conference. Pattee, Goel, Hufford, Klir.

Lerner, D: (1963) Parts and Wholes, Free Press, New York

* Lilienfeld, Robert: (1978) Rise of Systems Theory: An Ideological Analysis, Wiley-Intersciences, New York

A good critical view of some undesirable developments in the systems movement.

Lumsden, Charles, and Wilson, Edward: (1981) Genes, Mind, and Culture: the Coevolutionary Process, Harvard, Cambridge

Non-systemic attempt at unified biological evolutionary theory. Mind as necessary explanatory component from genes to culture. Sociobiology, biological constraint and cause of behavior. Epigenetic rules, epigenesis as coevolution. Mathematical, culturgens. Euculture as human culture, vs. protoculture. Bibliography, no thermodynamics.

Mandelbrot, BB: (1982) Fractal Geometry of Nature, WH Freeman, San Francisco

Classical work on the implications of fractal geometry for modeling physical systems.

Margalef, D Ramon: (1968) Perspectives in Sociological Theory, U. Chicago, Chicago

Maturana, HR, and Varela, F: (1987) Tree of Knowledge, Shambala

On cybernetics and constructivist psychology.

McCulloch, Warren: (1965) Embodiments of Mind, MIT Press, Cambridge

Meadows, Donella H, and Meadows, Dennis L: (1972) Limits to Growth, Signet, New York, and its follow-up Beyond the Limits

Famous report of the Club of Rome. First systems dynamics model of world ecology.

Mesarovic, MD: (1964) Views of General Systems Theory, Wiley, New York

Mesarovic, MD, and Macko, D: (1970) Theory of Hierarchical Multi-Level Systems, Academic Press, New York

Mesarovic, MD, and Takahara, Y: (1975) General Systems Theory: Mathematical Foundations, Academic Press, New York

Mesarovic, MD, and Takahara, : (1988) Abstract Systems Theory, Springer-Verlag, Berlin

Grand formalism for Systems Science. Fundamental behaviorism. Teleogical (functional) and material, causal (structural) descriptions as equivalent in system-description language. Defense of formalism as a kind of language. Systems as proper relations. Cybernetic systems as goal-seeking. Complexity as meta-systems (nesting). Introductions to category theory, topology, etc. Fuzzy systems as ** open** systems.

* Miller, James G: (1978) Living Systems, McGraw Hill, New York

General synthetic theory of biological systems. On functional self-similarity across levels of analysis.

Miser, HJ, and Quade, ES: eds. (1985) Handbook of Systems Analysis, North-Holland, New York

Monod, Jacques: (1971) Chance and Necessity, Vantage, New York

Famous essay on philosophical problems concerning theories of biological systems.

Morowitz, Harold J: (1968) Energy Flow in Biology, Academic Press, New York

On the thermodynamics and informational (entropic) dynamics of biological processes.

Morrison, P: (1982) Powers of Ten, in: Scientific American Books, WH Freeman, New York

"Guided tour" through the spatial scales of natural structure.

Negoita, CV, and Ralescu, DA: (1975) Applications of Fuzzy Sets to Systems Analysis, Birkhauser, Stuttgart

* Negotia, CV: (1981) Fuzzy Systems, Abacus Press, Tunbridge-Wells

Simple, coherent introduction to fuzzy systems theory.

Nicolis, G, and Prigogine, Ilya: (1977) Self-Organization in Non-Equilibrium Systems, Wiley, New York

Technical work on self-organization in flow systems, thermodynamic systems, and other describably in terms of partial differential equations.

* Odum, HT: (1983) Systems Ecology, Wiley, New York

Grand theory of global ecology. Thermodynamic basis of economy.

Pattee, Howard: ed. (1973) Hierarchy Theory, George Braziller, New York

Phillips, DC: (1976) Holistic Thought in Social Sciences

On synthesis on holism and reductionism.

Pines, David: ed. (1988) Emerging Syntheses in Science, Addison-Wesley, New York

Includes key articles by Charles Bennett and interesting looks at spin-glasses and solitons.

Powers, WT: (1973) Behavior, the Control of Perception, Aldine, Chicago

* Prigogine, Ilya: (1980) From Being to Becoming, WH Freeman, San Francisco

On the whole Prigogine program for explanation of evolution in thermodynamic terms.

..... (1984) Order Out of Chaos, Bantam, New York

Famous, almost-popular treatment of the relation between far-from-equilibrium thermodynamic, general evolutionary theory, and natural philosophy.

Rapoport, Anatol: (1984) General Systems Theory: Essential Concepts and Applications, Abacus, Cambridge

Rescher, Nicholas: Scientific Explanation

Uses stochastic automata in a philosophy of theory.

..... (1979) Cognitive Systematization, Rowman and Littlefie, Totowa, NJ

Treatment of coherentist epistemolgoy and formal development of the necessary limits to knowledge.

Rosen, Robert: (1970) Dynamical Systems Theory in Biology, Wiley-Interscience, New York

..... (1985) Anticipatory Systems, Pergamon, Oxford

The only book on anticipatory systems at present.

Rosenkrantz, Roger D: ed. (1989) ET Jaynes Papers on Prob., Statistics and Statistical Physics, Kluwer

Collection of Jayne's best papers.

Sage, AP: (1977) Methodology for Large Scale Systems, McGraw-Hill, New York

Sandquist, GM: (1985) Introduction to Systems Science, Prentice Hall, Eng. Cliffs NJ

* Sayre, Kenneth: (1976) Cybernetics and the Philosophy of Mind, Humanities Press, Atl. High., NJ

Grand cybernetic evolutionary theory of mind.

Schrodinger, : (1967) What is Life?, Cambridge U., Cambridge,

Classic essay series on foundations of biological theory.

Shafer, Glen: (1976) A Mathematical Theory of Evidence, Princeton U., Princeton

On the foundations of extended information theory, in particular extended probabilities and Dempster-Shafer evidential inference.

Shannon, CE: ed. (1956) Automata Studies, Princeton U. Press, Princeton

First and historically very important book on automata; includes von Neumann on probabilistic automata.

Shannon, CE, and Weaver, W: (1964) Mathematical Theory of Communication, U. Illinois, Urbana

Classic work on the foundations of classical information theory.

Simon, Herbert: (1969) Sciences of the Artificial, MIT, Boston

..... (1977) Models of Discovery, Reidel, Boston

Skilling, John: ed. (1989) Maximum-Entropy and Bayesian Methods, Kluwer

Proceedings of the 8th MaxEnt workshop. Statistical thermodynamics and quantum mechanics. Measurement, crystalography, spectroscopy, time series, power spectra, astronomy, neural nets. Fundamentals, statistics.

Skoglund, V: (1967) Similitude: Theory and Applications, Int. Textbook Co., Scranton PA

Smuts, JC: (1926) Holism and Evolution, McMillan, London

Early work on holism.

Steinbruner, JD: (1974) Cybernetic Theory of Decision, Princeton U, Princeton

Susiluoto, I: (1982) Origins and Development of Systems Thinking in USSR, in: Annales Acad. Scie.,Diss.HumanLitt., v. 30, Finnish Acad. Sci., Helsinki

* Szucz, E: (1980) Similitude and Modeling, Elsevier, New York,

Probably the most modern book on the theory of similarity.

Theil, H: (1967) Economics and Information Theory, Rand McNally, Chicago

Classic work on the use of information theory in economic theory.

..... (1983) Mathematical Models of Morphogenesis, Ellis Hortwood Ltd., New York

Topological approach to systems philosophy, catastrophe theory, dynamical systems.

Thompson, D'Arcy: (1959) On Growth and Form, Cambridge U., Cambridge

Classic work in early cybernetic biological theory.

Trappl, Robert: ed. (1983) Cybernetics: Theory and Applications, Hemisphere, Washington

Anthology of foundations of systems and cybernetics; review of applications; complete bibliography. Beer, Atlan, Pichler, Klir, Pask, Nowakowska, Arbib, Laszlo.

Trappl, Robert, and Horn, W, et. al.: eds. (1984) Basic and Applied General Systems Research: Bibliography, IFSR, Laxenburg,Aust., NOTE: From 1977-1984

Turchin, Valentin: (1977) Phenomenon of Science, Columbia U., New York

Cybernetic theory of universal evolution. Metascience as a cybernetic enterprise.

..... (1981) Inertia of Fear and Scientific Worldview, Columbia U. Press, New York
Interpretation of totalitarianism from the persepctive of cybernetic social theory.

** von Bertalanffy, Ludwig: (1968) General Systems Theory, George Braziller, New York

Van Laarhove, PJ, and Aarts, EHL: (1987) Simulated Annealing: Theory and Applications, Kluwer

On phase transitions.

Varela, FG: (1979) Principles of Biological Autonomy, North Holland, New York

von Foerster, Heinz: (1979) Cybernetics of Cybernetics, ed. K. Krippendorf, GordonandBreach, New York

ed. (1981) Observing Systems, Intersystems, Seaside CA

Many classic early cybernetics essays. On self-organization,memory without record (non-localized representation), computation of neural nets, necessities of biological function. Later essays on self-referential psychology much weaker.

von Foerster, Heinz, and Zopf, G.: eds. (1962) Principles of Self-Organization, Pergamon, New York

von Neumann, John: (1958) Computer and the Brain, Yale U., New Haven

Classical essay on the theoretical foundations of cognitive science.

..... (1966) Theory of Self-Reproducing Automata, U. Illinois, Urbana Ill.

Waddington, CH: (1977) Tools for Thought, Cape, London

Warfield, JN: (1976) Societal Systems, Wiley-Interscience, New York

Wartofsky, MW: (1979) Models, Reidel, Boston

Weber, Bruce: ed. (1988) Entropy, Information, and Evolution, MIT Press, Cambridge

Primary reference. Wicken, Wiley, Brooks. Cities as dissipative structures.

* Weinberg, Gerard M.: (1975) An Introduction to General Systems Thinking, Wiley, New York

..... (1988) Rethinking Systems Analysis and Decision, Dorset House, New York

Weir, M: (1984) Goal-Directed Behavior, Gordon and Breach

Wicken, Jeffrey: (1987) Evolution, Information and Thermodynamics, Oxford U., New York

** Wiener, Norbert: (1948) Cybernetics. or Control and Communication in the Animal and Machine, MIT Press, Cambridge On the Human Use of Human Beings: Cybernetics and Society

Wilden, Anthony: (1972) System and Structure, Tavistock, New York

Series of fascinating, polemical essays on a vast variety of subjects critical to systems and cybernetics and their relations to depth psychology, politics, and world culture.

Wilson, B: (1984) Systems: Concepts, Methodologies, and Applications, Wiley, Chichester UK

Windeknecht, TG: (1971) General Dynamical Processes, Academic Press, New York

Wolfram, Steven: ed. (1986) Theory and Applications of Cellular Automata, Scientific Press

Wymore, AW: (1969) Mathematical Theory of Systems Engineering, Wiley, New York

..... (1976) Systems Engineering Methodology for Interdisc. Theory, Wiley, New York

Wymore, Wayne: Systems Theory

Yates, Eugene: ed. (1987) Self-Organizing Systems: the Emergence of Order, Plenum, New York

Critical collection on self-organizing systems. Includes Iberall, Morowitz, Arbib, Pattee, Haken, Caianiello, Abraham and Shaw.

Zadeh, Lofti A: (1954) System Theory, in: Columbia Eng. Quart., v. 8, pp. 16-19

Zadeh, Lofti A, and Desoer, CA: (1963) Linear Systems Theory, Mcraw-Hill, New York

Development of hysteresis and catastrophe theory as a modeling tool for systems science.

Zeigler, BP: (1976) Theory of Modeling and Simulation, Wiley, New York

Zeigler, BP, and Elzas, MS et. al.: eds. (1979) Methodology in Systems Modeling and Simulation, North-Holland, New York

Zeleny, Milan: ed. (1981) Autopoiesis: A Theory of Living Organization, North-Holland, New York

Critical anthology on this theory of self-organization, including Maturana and Varela.

# Basic Papers on Cybernetics and Systems Science

Author: C. Joslyn & F. Heylighen
Updated: Jul 22, 1996
Filename: CSPAPER.html

The following is a list of references used for the course SS-501, INTRODUCTION TO SYSTEMS SCIENCE, at the Systems Science Department of SUNY Binghamton in 1990.

All of the classic, "required" papers have been reprinted in the book: Klir G.J. (1992) Facets of Systems Science, (Plenum, New York). You can order photocopies of many papers via the CARL UnCover service, which provides a search through a database containing millions of papers in thousands of academic journals covering all disciplines. Other, specific bibliographic references of books and a selected number of papers can be found in the library database of the Department of Medical Cybernetics and AI at the University of Vienna. A number of more recent books and papers can be found in our bibliography on complex, evolving systems, and in the bibliography of the Principia Cybernetica Project.


Key:    *       Required
R       Recommended

Abraham, Ralph: (1987) "Dynamics and Self-Organization", in:
/Self-Organizing Systems/, ed. Eugene Yates, pp. 599-616, Plenum

Review of the scope and extent of modern dynamics theory,
especially as related to problems in self-organization.  Useful
after an elementary understanding of dynamical systems.

Abraham, Ralph, and Shaw, Christophe: (1987) "Dynamics: a
Visual Introduction", in: /Self-Organizing Systems: Emergence/,
ed. Eugene Yates, pp. 543-598, Plenum

Ackoff, Russel: (1979) "Future of Operational Research is
Past", /General Systems Yearbook/, v. 24, pp. 241-252

R Arbib, Michael A: (1966) "Automata Theory and Control Theory: A
Rapproachement", /Automatica/, v. 3, pp. 161-189

A unification of automata theory and control theory in a broader
theory of dynamic systems.

Arbib, Michael A, and Rhodes, JL et. al.: (1968) "Complexity and
Graph Complexity of Finite State Machines and Finite Semi-Groups",
in: /Algorithmic Theory of Machines, Languages and Semi-Groups/,
ed. MA Arbib, pp. 127-145, Academic Press, New York

A rigorous formulation of descriptive complexity of systems in
terms of finite state machines.

* Ashby, Ross: (1958) "General Systems Theory as a New
Discipline", /General Systems Yearbook/, v. 3:1

* (1958) "Requisite Variety and Implications for Control of Complex
Systems", /Cybernetica/, v. 1, pp. 83-99

* (1964) "Introductory Remarks at Panel Discussion", in: /Views
in General Systems Theory/, ed. M. Mesarovic, pp. 165-169, Wiley,
New York

(1965) "Measuring the Internal Informational Exchange in a
System", /Cybernetica/, v. 1, pp. 5-22

A readable paper that explains how the Shannon entropy can be
used in analyzing systems.

(1968) "Some Consequences of Bremermann's Limit for Information
Processing Systems", in: /Cybernetic Problems in Bionics/, ed.
H Oestreicher et. al, pp. 69-76, Gordon and Breach, New York

(1970) "Information Flows Within Coordinated Systems", /Progress
in Cybernetics/, v. 1, ed. J. Rose, pp. 57-64, Gordon and Breach,
London

(1972) "Systems and Their Informational Measures", in: /Trends in
General Systems Theory/, ed. GJ Klir, pp. 78-97, Wiley, New York,

* (1973) "Some Peculiarities of Complex Systems", /Cybernetic
Medicine/, v. 9:2, pp. 1-6

Atlan, Henri: (1981) "Hierarchical Self-Organization in Living
Systems", in: /Autopoises/, ed. Milan Zeleny, North Holland, New
York

Auger, Peter: (1989) "Microcanonical Ensembles with
Non-equiprobable States", /Int. J. Gen. Sys./, v. 20:3, pp.
457-466

Aulin, AY: (1975) "Cybernetics as Foundational Science of
Action", /Cybernetic/, v. 3

* (1979) "Law of Requisite Hierarchy", /Kybernetes/, v. 8, pp.
259-266

Bahm, AJ: (1981) "Five Types of Systems Philosophies", /Int. J.
Gen. Sys./, v. 6

(1983) "Five Systems Concepts of Society", /Behavoral Science/,
v. 28

(1984) "Holons: Three Conceptions", /Systems Research/, v. 1:2,
pp. 145-150

Comparison of three system philosophies.

(1986) "Nature of Existing Systems", /Systems Research/, v. 3:3,
Pergamon, Oxford

Philosophical analysis of the necessary and sufficient conditions
for systemic processes.

(1988) "Comparing Civilizations as Systems", /Systems Research/,
v. 5:1

Macroscopic structural, semantic analysis of cultural systems.

Bailey, Kenneth D: (1984) "Equilibrium, Entropy and
Homeostasis", /Systems Research/, v. 1:1, pp. 25-43

Excellent survey of these concepts in multiple disciplines.

Balakrishnan, AV: "On the State Space Theory of Linear
Systems", /J. Mathematical Analysis and Appl./, v. 14:3, ed.
1966, pp. 371-391

* Barto, AG: (1978) "Discrete and Continuous Model", /Int. J.
Gen. Sys./, v. 4:3, pp. 163-177

Bennett, Charles H: (1986) "On the Nature and Origin of Complexity
in Discrete, Homogeneous, Locally-Interacting Systems",
/Foundations of Physics/, v. 16, pp. 585-592

On Bennett's measure of algorithmic depth.

Black, M: (1937) "Vagueness: An Exercise in Logical Analysis",
/Philosophy of Science/, v. 4, pp. 427-455

Probably the best discussion of the meaning of vagueness and its
importance in science and philosophy.

* Boulding, Ken: (1956) "General Systems Theory - The Skeleton of
Science", /General Systems Yearbook/, v. 1, pp. 11-17

(1968) "Specialist with a Universal Mind", /Management Science/,
v. 14:12, pp. B647-653

* (1974) "Economics and General Systems", /Int. J. Gen. Sys./, v.
1:1, pp. 67-73

Bovet, DP: (1988) "An Introduction to Theory of Computational
Complexity", in: /Measures of Complexity/, ed. L Peliti, A
Vulpiani, pp. 102-111, Springer-Verlag, New York

Braitenberg, Valentino: "Vehicles: Expirement in Synthetic
Psychology", /IEEE Trans. of Syst., Man, and Cyb./

On the complex, seemingly lifelike behavior of simply designed
cybernetic robots.

* Bremermann, HJ: (1962) "Optimization Through Evolution and
Recombination", in: /Self-Organizing Systems/, ed. MC Yovits et.
al., pp. 93-106, Spartan, Washington DC

(1967) "Quantifiable Aspects of Goal-Seeking Self-Org. Systems",
in: /Progress in Theoretical Biology/, v. M Snell, pp. 59-77,

Brillouin, Leon: (1953) "Negentropy Principle of Information",
/J. of Applied Physics/, v. 24:9, pp. 1152-1163

First Brillouin essay, on the relation between thermodynamic and
informational entropies.

* Bunge, Mario: (1978) "General Sys. Theory Challenge to
Classical Philosophy of Science", /Int. J. Gen. Sys./, v. 4:1

(1981) "Systems all the Way", /Nature and Systems/, v. 3:1, pp.
37-47

Carnap, Rudolph, and Bar-Hillel, Y: (1952) "Semantic
Information", /British J. for Philosopy of Science/, v. 4, pp.
147-157

Cavallo, RE, and Pichler, F: (1979) "General Systems
Methodology: Design for Intuition Ampl.", in: /Improving the
Human Condition/, Springer-Verlag, New York

Caws, P: (1974) "Coherence, System, and Structure", /Idealistic
Studies/, v. 4, pp. 2-17

Chaitin, Gregory J: (1975) "Randomness and Mathematical Proof",
/Scientific American/, v. 232:5

(1977) "Algorithmic Information Theory", /IBM J. Res. Develop./,
v. 21:4, pp. 350-359

Introduction of Chaitin's version of Kolmogorov complexity.

(1982) "Godel's Theorem and Information", /Int. J. Theoretical
Physics/, v. 22

* Checkland, Peter: (1976) "Science and Systems Paradigm", /Int.
J. Gen. Sys./, v. 3:2, pp. 127-134

Chedzey, Clifford S, and Holmes, Donald S: (1976) "System
Entropies of Markov Chains", /General Systems Yearbook/, v. XXI,
pp. 73-85

(1977) "System Entropy and the Monotonic Approach to Equilibrium",
/General Systems Yearbook/, v. 22, pp. 139-142

(1977) "System Entropy of a Discrete Time Probability Function",
/General Systems Yearbook/, v. 22, pp. 143-146

(1977) "First Discussion of Markov Chain System Entropy Applied
to Physics", /General Systems Yearbook/, v. 22, pp. 147-167

Cherniak, Christophr: (1988) "Undebuggability and Cognitive
Science", /Communications of the ACM/, v. 31:4

Like Bremmerman's limit, some simple mathematics on the limits of
computational methods.

Christensen, Ronald: (1985) "Entropy Minimax Multvariate
Statistical Modeling: I", /Int. J. Gen. Sys./, v. 11

R Conant, Roger C: (1969) "Information Transfer Required in
Regulatory Processes", /IEEE Trans. on Sys. Sci. and Cyb./, v. 5:4,
pp. 334-338

A discussion of the use of the Shannon entropy in the study of
regulation.

R (1974) "Information Flows in Hierarchical Systems", /Int. J.
Gen. Sys./, v. 1, pp. 9-18

Using classical (Shannon) information theory, it is shown that
hierarchical structures are highly efficient in information
processing.

* (1976) "Laws of Information Which Govern Systems", /IEEE Trans.
Sys., Man & Cyb./, v. 6:4, pp. 240-255

* Conant, Roger C, and Ashby, Ross: (1970) "Every Good Regulator
of Sys. Must Be Model of that Sys.", /Int. J. Systems Science/,
v. 1:2, pp. 89-97

Cornacchio, Joseph V: (1977) "Systems Complexity: A
Bibliography", /Int. J. Gen. Sys./, v. 3, pp. 267-271

De Raadt, JDR: (1987) "Ashby's Law of Requisite Variety: An
Empirical Study", /Cybernetics and Systems/, v. 18:6, pp.
517-536

R Eigen, M, and Schuster, P: (1977) "Hypercycle: A Principle of
Natural Self-Org.", /Naturwissenschaften/, v. 64,65

Classical work on molecular feedback mechanisms.

Engell, S: (1984) "Variety, Information, and Feedback",
/Kybernetes/, v. 13:2, pp. 73-77

Erlandson, RF: (1980) "Participant-Oberver in Systems
Methodologies", /IEEE Trans. on Sys., Man, and Cyb./, v. SMC-10:1,
pp. 16-19

Ferdinand, AE: (1974) "Theory of Systems Complexity", /Int. J.
Gen. Sys./, v. 1:1, pp. 19-33

A paper that connects defect probability with systems complexity
through the maximum entropy principle.  Also investigates the
relationship between modularity and complexity.

Ford, Joseph: (1986) "Chaos: Solving the Unsolvable, Predicting
the Unpredictable", in: /Chaotic Dynamics and Fractals/,

Fascinating account of the relation between chaotic dynamics, the
limits of observability, constructive mathematics, exsitence and
uniqueness, and the "ideology" of the scientific community.

Gaines, Brian R: "An Overview of Knowledge Acquisition and Transfer",
/IEEE Proc. on Man and Machine/, v. 26:4

GSPS type methods as the general form of all science.  Relation
of Klir's GSPS methodology to other inductive methodologies.

R (1972) "Axioms for Adaptive Behavior", /Int. J. of Man-Machine
Studies/, v. 4, pp. 169-199

Perhaps the most comprehenseive foundational work on adaptive
systems.

* (1976) "On the Complexity of Causal Models", /IEEE Trans. on
Sys., Man, & Cyb./, v. 6, pp. 56-59

R (1977) "System Identification, Approximation and Complexity",
/Int. J. Gen. Sys./, v. 3:145, pp. 145-174

A thorough discussion on the relationship among complexity,
credibiilty, and uncertainty associated with systems models.

* (1978) "Progress in General Systems Research", in: /Applied
General Systems Research/, ed. GJ Klir, pp. 3-28, Plenum, New
York

* (1979) "General Systems Research: Quo Vadis?", /General Systems
Yearbook/, v. 24, pp. 1-9

* (1983) "Precise Past - Fuzzy Future", /Int. J. Man-Machine
Studies/, v. 19, pp. 117-134

* (1984) "Methodology in the Large: Modeling All There Is",
/Systems Research/, v. 1:2, pp. 91-103

R Gallopin, GC: "Abstract Concept of Environment", /Int. J. Gen.
Sys./, v. 7:2, pp. 139-149

A rare discussion of the concept of environment by a well known
ecologist.

Gardner, MR: (1968) "Critical Degenerotes in Large Linear
Systems", /BCL Report/, v. 5:8, EE Dept., U. Ill, Urbana

A report on an experimental investigation whose purpose is to
determine the relationship between stability and connectance of
linear systems.

* Gardner, MR, and Ashby, Ross: (1970) "Connectance of Large
Dynamic (Cybernetic) Systems", /Nature/, v. 228:5273, pp. 784

Gelfland, AE, and Walker, CC: (1977) "Distribution of Cycle
Lengths in Class of Abstract Sys.", /Int. J. Gen. Sys./, v. 4:1,
pp. 39-45

* Goguen, JA, and Varela, FJ: (1979) "Systems and Distinctions:
Duality and Complementarity", /Int. J. Gen. Sys./, v. 5:1, pp.
31-43

Gorelick, George: (1983) "Bogdanov's Tektology: Naure,
Development and Influences", /Studies in Soviet Thought/, v. 26,
pp. 37-57

Greenspan, D: (1980) "Discrete Modeling in Microcosm and
Macrocosm", /Int. J. Gen. Sys./, v. 6:1, pp. 25-45

* Hall, AS, and Fagan, RE: (1956) "Definition of System",
/General Systems Yearbook/, v. 1, pp. 18-28

Harel, David: (1988) "On Visual Formalisms", /Communications of
the ACM/, v. 31:5

Reasonable, critical extensions of "Venn Diagrams", general
consideration of the representation of multidimensional systems.

Henkind, Steven J, and Harrison, Malcolm C: (1988) "Analysis of
Four Uncertainty Calculi", /IEEE Trans. Man Sys. Cyb./, v. 18:5,
pp. 700-714

On Bayesian, Dempster-Shafer, Fuzzy Set, and MYCIN methods of
uncertainty management.

Herbenick, RM: (1970) "Peirce on Systems Theory", /Transaction
of the S. Peirce Soc./, v. 6:2, pp. 84-98

R Huber, GP: (1984) "Nature and Design of Post-Industrial
Organizations", /Management Science/, v. 30:8, pp. 928-951

Excellent paper discussing the changing nature of organizations
in the information society.

* Islam, S: (1974) "Toward Integrating Two Systems Theories By
Mesarovic and Wymore", /Int. J. Gen. Sys./, v. 1:1, pp. 35-40

Jaynes, ET: (1957) "Information Theory and Statistical
Mechanics", /Physical Review/, v. 106,108, pp. 620-630

A classic paper.  Information theory as a sufficient and elegant
basis for thermodynamics.  But does it follow that thermodynamics
is necessarily dependent on information theory, or that entropy
is "just" incomplete knowledge?  Compares principle of maximum
entropy with assumptions of ergodicity, metric transitivity,
and/or uniform a priori distributions.  Prediction as microscopic
to macroscopic explanation; interpretation as macro to micro.

Johnson, Horton A.: (1970) "Information Theory in Biology After
18 Years", /Science/, v. 6/26/70

Scathing critique of the role of "classical" information theory
in biological science.  Most of these criticisms are still
algorithmic complexity theory).

Joslyn, Cliff: (1988) "Review: Works of Valentin Turchin",
/Systems Research/, v. 5:1

Short introduction to Turchin's cybernetic theories of universal
evolution.

* Kampis, G: (1989) "Two Approaches for Defining 'Systems'",
/Int. J. Gen. Sys./, v. 15, pp. 75-80

Kaufmann, Stuart A: (1969) "Metabolic StabilityandEpigenesis in
Randomly Constructed Genetic Nets", /Journal of Theoretical Biology/,
v. 22, pp. 437-467

(1984) "Emergent Properties in Random Complex Automata",
/Physica/, v. 10D, pp. 145

Kellerman, E: (1968) "Framework for Logical Cont.", /IEEE
Transactions on Computers/, v. E-17:9, pp. 881-884

Klapp, OE: (1975) "Opening and Closing in Open Systems",
/Behav. Sci./, v. 20, pp. 251-257

Philosophy on the dynamics of social processes; entropic
metaphors.

R Klir, George: (1970) "On the Relation Between Cybernetics and
Gen. Sys. Theory", in: /Progress in Cybernetics/, v. 1, ed. J
Rose, pp. 155-165, Gordon and Breach, London

A formal discussion on the relation between the fields of
"cybernetics" and "systems science", concluding that the former
is a subfield of the latter.

(1972) "Study of Organizations of Self-Organizing Systems", in:
/Proc. 6th Int. Congress on Cyb./, pp. 162-186, Wammer, Belgium

(1976) "Ident. of Generative Structures in Empirical Data", /Int.
J. Gen. Sys./, v. 3:2, pp. 89-104

(1978) "General Systems Research Movement", in: /Sys. Models for
Decision Modeling/, ed. N Sharif et. al., pp. 25-70, Asian Inst.
Tech., Bangkok

* (1985) "Complexity: Some General Observations", /Systems
Research/, v. 2:2, pp. 131-140

* (1985) "Emergence of 2-D Science in the Information Society",
/Systems Resarch/, v. 2:1, pp. 33-41

* (1988) "Systems Profile: the Emergence of Systems Science",
/Systems Research/, v. 5:2, pp. 145-156

Klir, George, and Way, Eileen: (1985) "Reconstructability
Analysis: Aims, Results, Problems", /Systems Research/, v. 2:2,
pp. 141-163

Introduction to the methods of reconstruction as well as their
relevance to general philosophical problems.

Kolmogorov, AN: (1965) "Three Approaches to the Quantitative Definition
of Information", /Problems of Information Transmission/, v. 1:1,
pp. 1-7

First introduction of algorithmic meatrics of complexity and
information.

Krippendorf, Klaus: (1984) "Epistemological Foundation for
Communication", /J. of Communication/, v. 84:Su

On the necessary cybernetics of communication.

Krohn, KB, and Rhodes, JL: (1963) "Algebraic Theory of
Machines", in: /Mathematical Theory of Automata/, ed. J. Fox, pp.
341-384, Ploytechnic Press, Brooklyn NY

(1968) "Complexity of Finite Semigroups", /Annals of
Mathematics/, v. 88, pp. 128-160

Layzer, David: (1988) "Growth of Order in the Universe", in:
/Entropy, Information, and Evolution/, ed. Bruce Weber et. al.,
pp. 23-40, MIT Press, Cambridge

On the thermodynamics of cosmological evolution, and the
necessity of "self-organization" in an expanding universe.

* Lendaris, GG: (1964) "On the Definition of Self-Organizing
Systems", /IEEE Proceedings/, v. 52, pp. 324-325

R Lettvin, JY, and Maturana, HR: (1959) "What the Frog's Eye Tell
the Frog's Brain", /Proceedings of the IRE/, v. 47, pp.
1940-1951

Classic early paper in cybernetics, cited as the basis of
"constructive" psychological theory.

Levin, Steve: (1986) "Icosahedron as 3D Finite Element in
Biomechanical Supp.", in: /Proc. 30th SGSR/, v. G, pp. 14-23

R (1989) "Space Truss as Model for Cervical Spine Mechanics",
NOTE: Manuscript

Startling theory of the necessary foundations of biomechanics in
2-d triangular (heaogonal) plane packing and 3-d dodecahedral
space packing.

Lloyd, Seth, and Pagels, Heinz: (1988) "Complexity as
Thermodynamic Depth", /Annals of Physics/, v. 188, pp. 1

Perhaps a classic, on their new measure as the difference betwen
fine and coarse entropy.  Comparison with other measures of
depth, complexity, and information.

* Lofgren, Lars: (1977) "Complexity of Descriptions of Sys: A
Foundational Study", /Int. J. Gen. Sys./, v. 3:4, pp. 197-214

Madden, RF, and Ashby, Ross: (1972) "On Identification of
Many-Dimensional Relations", /Int. J. of Systems Science/, v. 3,
pp. 343-356

An early paper contributing to the area that is known now as
reconstructibility analysis.

Makridakis, S, and Faucheux, C: (1973) "Stability Properties of
General Systems", /General Systems Yearbook/, v. 18, pp. 3-12

Makridakis, S, and Weinstraub, ER: (1971) "On the Synthesis of
General Systems", /General Systems Yearbook/, v. 16, pp. 43-54

Margalef, D Ramon: (1958) "Information Theory in Ecology",
/General Systems Yearbook/, v. 3, pp. 36-71

* Marschal, JH: (1975) "Concept of a System", /Philosophy of
Science/, v. 42:4, pp. 448-467

* May, RM: (1972) "Will a Large Complex System be Stable?",
/Nature/, v. 238, pp. 413-414

McCulloch, Warren, and Pitts, WH: "Logical Calculus of Ideas
Immanent in Nervous Activity", /Bull. Math. Biophysics/, v. 5

Classic early work on the neural nets as a logical modeling
tool.

McGill, WJ: (1954) "Multivariate Information Transmission",
/Psychometrica/, v. 19, pp. 97-116

Mesarovic, MD: (1968) "Auxiliary Functions and Constructive
Specification of Gen. Sys.", /Mathematical Systems Theory/,
v. 2:3

R Miller, James G: (1986) "Can Systems Theory Generate Testable
Hypotheses?", /Systems Research/, v. 3:2, pp. 73-84

On systems theoretic research programs attempting to unify
scientific theory through hypothesized isomorphies among levels
of analysis.

Negoita, CV: (1989) "Review: Fuzzy Sets, Uncertainty, and
Information", /Kybernetes/, v. 18:1, pp. 73-74

Good analysis of the significance of fuzzy set theory.

Pattee, Howard: "Evolution of Self-Simplifying Systems", in:
/Relevance of GST/, ed. Ervin Laszlo, George Braziller, New York,

"Instabilities and Information in Biological Self-Organization",
in: /Self Organizing Systems/, ed. F. Eugene Yates, Plenum, New York

(1973) "Physical Problems of Origin of NaturalControl", in:
/Biogenesis, Evolution, Homeostasis/, ed. A. Locker,
Springer-Verlag, New York

(1978) "Complementarity Principle in Biological and Social Structures",
/J. of Social and Biological Structures/, v. 1

(1985) "Universal Principle of Measurement and Language Function in
Evolving Systems", in: /Complexity, Language, and Life/, ed. John
Casti, pp. 268-281, Springer-Verlag, Berlin

(1988) "Simulations, Realizations, and Theories of Life", in:
/Artificial Life/, ed. C Langton, pp. 63-77, Addison-Wesley,
Redwood City CA

Patten, BC: (1978) "Systems Approach to the Concept of
Environment", /Ohio J. of Science/, v. 78:4, pp. 206-222

Pearl, J: (1978) "On Connection Between Complexity and Credibility of
Inferred Models", /Int. J. Gen. Sys./, v. 4:4, pp. 255-264

Theoretical study that shows that credibility of determinstic
models inferred from data tends to increase with data size and
decrease with the complexity of the model.

Pedrycz, W: (1981) "On Approach to the Analysis of Fuzzy
Systmes", /Int. J. of Control/, v. 34, pp. 403-421

Peterson, JL: (1977) "Petri Nets", /ACM Computing Surveys/, v.
9:3, pp. 223-252

R Pippenger, N: (1978) "Complexity Theory", /Scientific
American/, v. 238:6, pp. 114-124

Excellent discussion of one facet of complexity.

* Porter, B: (1976) "Requisite Variety in the Systems and Control
Sciences", /Int. J. Gen. Sys./, v. 2:4, pp. 225-229

Prigogine, Ilya, and Nicolis, Gregoire: (1972) "Thermodynamics
of Evolution", /Physics Today/, v. 25, pp. 23-28

Briefer introduction to far-from-equilibrium thermodynamics,
hypercycles, and evolutionary theory.  Criticzed as confused.

Rapoport, Anatol: (1962) "Mathemantical Aspects of General
Systems Theory", /General Systems Yearbook/, v. 11, pp. 3-11

Rivier, N: (1986) "Structure of Random Cellular Networks and
Their Evolution", /Physica/, v. 23D, pp. 129-137

Brilliant introduction to the theory of the equilibrium
distribution of macroscopic entities (cells) in multiple kinds of
substances: metals, soap suds, and animal and vegetable tissues;
according to a non-thermodynamic maximum entropy law.  Subsumes
other laws from these specific disciplines.

(1988) "Statistical Geometry of Tissues", in: /Thermodynamics and
Pattern Formation in Biology/, pp. 415-445, Walter de Gruyter,
New York

* Rosen, Robert: (1977) "Complexity as a Systems Property", /Int.
J. Gen. Sys./, v. 3:4, pp. 227-232

* (1978) "Biology and Systems Resarch", in: /Applied General
Systems Research/, ed. GJ Klir, pp. 489-510, Plenum, New York

* (1979) "Anticipatory Systems", /General Systems Yearbook/, v.
24, pp. 11-23

* (1979) "Old Trends and New Trends in General Systems Resarch",
/Int. J. Gen. Sys./, v. 5:3, pp. 173-184

* (1981) "Challenge of Systems Theory", /General Systems
Bulletin/

* (1985) "Physics of Complexity", /Systems Research/, v. 2:2, pp.
171-175

* (1986) "Some Comments on Systems and Systems Theory", /Int. J.
Gen. Sys./, v. 13:1, pp. 1-3

Rosenbluth, Arturo, and Wiener, Norbert: (1943) "Behavior,
Purpose, and Teleology", /Philosophy of Science/, v. 10, pp.
18-24

Original introduction of teleonomy, teleology, goal-seeking, and
intentionality in cybernetic terms.

Rothstein, J: (1979) "Generalized Entropy, Boundary Conditions,
and Biology", in: /Maximum Entropy Formalism/, ed. RD Levine, pp.
423-468, Cambridge U., Cambridge

On boundary conditions in biology, organisms as "well-informed
heat engines", definition of mutual information, order as an
entropy measure.

Sadovsky, V: (1979) "Methodology of Science and Systems
Approach", /Social Science/, v. 10, Moscow

Saperstein, Alvin M.: (1984) "Chaos: A Model for the Outrbreak
of War", /Nature/, v. 309

Schedrovitzk, GP: (1962) "Methdological Problems of Systems
Research", /General Systems Yearbook/, v. 11, pp. 27-53

Schneider, Eric D: (1988) "Thermodynamics, Ecological Succession and
Natural Selection: A Common Thread", in: /Entropy, Information, and
Evolution/, pp. 107-138, Bruce Weber et. al., Cambridge

On the thermodynamics of maturing ecosystems, relation to
Principle of Maximum Entropy Production.

Shaw, Robert: (1981) "Strange Attractors, Chaotic Behavior and
Information Flow", /Zeitschrift fur Naturforschung/, v. 36a

(1984) /Dripping Faucet as a Model Chaotic System/, Aeriel Press,
Santa Cruz

Best explanation of the nature of chaotic processes, especially
with respect to information theory.

Simon, Herbert: (1965) "Architecture of Complexity", /General
Systems Yearbook/, v. 10, pp. 63-76

* (1988) "Predication and Prescription in Systems Modeling",
NOTE: IIASA manuscript

Skarda, CA, and Freeman, WJ: (1987) "How Brains Make Chaos Into
Order", /Behavioral and Brain Sciences/, v. 10

Interpretation of neurological experiments revealing the
cybernetic basis of perception, the reliance on chaotic dynamics,
and the non-locality of mental representations.  Resting as
chaos, perception as stable attractors, seizures as cyclic
attractos.

Skilling, John: (1989) "Classic Maximum Entropy", in: /Maximum
Entropy and Bayesian Methods/, ed. J. Skilling, pp. 45-52, Kluwer,
New York

Mathematical introduction to the traditonal MaxEnt method as
applied to data analysis.

Smith, C Ray: (1990) "From Rationality and Consistency to
Bayesian Probability", in: /Maximum Entropy and Bayesian Methods/,
ed. P. Fougere, Kluwer, New York

Mathematical introduction to the relation between inductive and
deductive reasoning, Cox's axioms, Bayes' theorem, and Jaynes'
MaxEnt program.

Smith, RL: (1989) "Systemic, not just Systematic", /Systems
Research/, v. 6:1, pp. 27-37

* Svoboda, A: "Model of the Instinct of Self-Preservation", in:
/MISP: A Simulation of a Model.../, ed. KA Wilson, NOTE: From
French,Inf.Proc.Mach. 7

Swenson, Rod: (1989) "Emergent Attractors and Law of Maximum Entropy
Production", /Systems Research/, v. 6:3, pp. 187-198

Good references for general evolution.  Discussion of minimax
entropy production and emergence, biological thermodynamics.

Szilard, L: (1964) "On Decrease of Enteopy in Thermodynamic Systems by
Intervention of Intelligent Beings", /Behavioral Science/, v. 9

Classic first paper on the necessary relation between
informational and thermodynamic entropies.

Takahara, Y, and Nakao, B: (1981) "Characterization of
Interactions", /Int. J. Gen. Sys./, v. 7:2, pp. 109-122

Takahara, Y, and Takai, T: (1985) "Category Theoretical
Framework of General Systems", /Int. J. Gen. Sys./, v. 11:1, pp.
1-33

Thom, Rene: (1970) "Topological Models in Biology", in:
/Towards a Theoretical Biology/, v. 3, ed. CH Waddington, Aldine,
Chicago

On self-simplifying systems.

Tribus, Myron: (1961) "Information Theory as the Basis for
Thermostatistics and Thermodynamics", /J. Applied Mechanics/, v. 28,
pp. 108

Full description of the derivation of basic thermodynamics from
Jayne's maximum entropy formalism.

R Turchin, Valentin: (1982) "Institutionalization of Values",
/Worldview/, v. 11/82

Review of Turchin's social theory and defense of reviews of
_Phenomenon of Science_ and _Inertia of Fear_.

(1987) "Constructive Interpretation of Full Set Theory", /J. of
Symbolic Logic/, v. 52:1

Almost complete reconstruction of ZF set theory from a
constructivist philosophy, including implementation in the REFAL
language.

Turney, P: (1989) "Architecture of Complexity: A New
Blueprint", /Synthese/, v. 79:3, pp. 515-542

Ulanowicz, R, and Hennon, B: (1987) "Life and Production of
Entropy", /Proc. R. Soc. London/, v. B 232, pp. 181-192

Excellent: principle of maximum entropy production; positive
feedback=autocatalysis; lasers as high dissipative, low entropy
producing systems; nuclear autocatalysis as greatest source of
entropy production; measurement techniques for biotic entropy
production; all chemical organization as either extinct or in
organisms; high efficiency as high entropy production; on metrics
of evolution.

v Bertalanfy, Ludwig: (1950) "An Outline of General Systems
Theory", /British J. of Philosophy of Science/, v. 1, pp.
134-164

(1962) "General Systems Theory - A Criticial Review", /General
Systems Yearbook/, v. 7, pp. 1-20

* Varela, FG, and Maturana, HR et. al.: (1974) "Autopoiesis: the
Organization of Living Systems, its Characterization, and a Model",
/Biosystems/, v. 5, pp. 187-196

First definition of autopoeisis.

von Foerster, Heinz: (1960) "On Self-Organizing Systems and
their Environments", in: /Self-Organizing Systems/, ed. Yovitz and
Cameron, Pergamon

Well written, many interesting observations.  Proof of
meaningless of the term "SOS", first (?) discussion of "growth of
phase space" route to organization, on relative information,
order from noise principle.

von Neumann, John: (1963) "General and Logical Theory of
Automata", in: /Collected Works/, v. 5, ed. AH Taub, pp. 288-328,
Pergamon

Classic.  On thermodynamics and fundamental cybernetics,
digital/analog distinctions and relations in complex systems.

(1963) "Probability, Logic, and Synthesis of Reliable Organization
from Unreliable Parts", in: /Collected Works/, v. 5, ed. AH Taub,
pp. 329-378, Pergamon

On logics, automata, and information theory.

* Waelchli, F: (1989) "Eleven Theses of General Systems Theory",
/Systems Research/, NOTE: To appear

R Walker, CC: (1971) "Behavior of a Class of Complex Systems",
/J. Cybernetics/, v. 1:4, pp. 55-67

A good example of the use of the computer in discovering systems
science laws.

Walker, CC, and Ashby, Ross: (1966) "On Temporal Characteristics of
Behavior in Certain Complex Systems", /Kybernetik/, v. 3:2, pp.
100-108

Warfield, JN, and Christakis, AN: (1986) "Dimensionality",
/Systems Research/, v. 3:3, Pergamon, Oxford

R Weaire, D, and Rivier, N: (1984) "Soaps, Cells and Statistics:
Random Patterns in 2-D", /Contemporary Physics/, v. 25:1, pp.
59-99

Continution of Rivier 1986.

* Weaver, Warren: (1968) "Science and Complexity", /American
Scientist/, v. 36, pp. 536-544

From Klir, on organized simplicity, unorganized complexity, and
organized complexity.

White, I: (1988) "Limits and Capabilities of Machines: A
Review", /IEEE Trans. on Sys., Man, and Cyb./, v. 18:6, pp.
917-938

Wicken, Jeffrey: (1987) "Entropy and Information: Suggestions
for a Common Language", /Philosphy of Science/, v. 54:2, pp.
176-193

Solid paper on more modern view of the relation between
thermodynamics and information theory.

Wilson, David S: (1989) "Reviving the Superorganism", /J.
Theor. Bio./, v. 136, pp. 337-356

On levels of selection, criteria for being an organism, systems
vs. aggregates.  Wilson is a current SUNY faculty.

Wolfram, Stephen: (1988) "Complex Systems Theory", in:
/Emerging Syntheses in Science/, ed. David Pines, pp. 183-190,

Example of a more simplistic appeal to entropy as a metric of
order.

Zadeh, Lofti A: (1958) "On the Identification Problem", /IRE
Trans. on Circuit Theory/, v. CT-3, pp. 277-281

* (1962) "From Circuit Theory to Systems Theory", /IRE
Proceedings/, v. 50, pp. 856-865

* (1963) "On the Definition of Adaptibility", /IEEE Proceedings/,
v. 51, pp. 469-470

(1963) "General Identification Problem", in: /Proceedings of the
Princeton Conference on the Identification Problem in Communications
and Control/, pp. 1-17

(1965) "Fuzzy Sets and Systems", in: /Systems Theory/, ed. J.
Fox, pp. 29-37, Polytechnic Press, Brooklyn NY

R (1973) "Outline of a New Approach to Analysis of Complex Sys.",
/IEEE Trans. on Sys., Man and Cyb./, v. 1:1, pp. 28-44

A motivation for using fuzziness in dealing with very complex
systems is discussed in detail.

(1982) "Fuzzy Systems Theory: Framework for Analysis of Buerocratic
Systems", in: /Sys. Meth. in Social Science Res./, ed. RE Cavallo,
pp. 25-41, Kluwer-Nijhoff, Boston

R Zeigler, BP: (1974) "Conceptual Basis for Modeling and
Simulation", /Int. J. Gen. Sys./, v. 1:4, pp. 213-228

A solid systems science conceptual framework for modeling and
simulation is introduced.

R (1976) "Hierarchy of Systems Specifications and Problems of
Structural Inference", in: /PSA 1976/, v. 1, ed. F.Suppe,
PD Asquith, pp. 227-239, Phil. Sci. Assoc., E. Lansing

Introduces a hierarchy of systems types (a formal treatment).

Zeleny, Milan: (1979) "Special Book Review", /Int. J. Gen.
Sys./, v. 5, pp. 63-71

(1988) "Tectology", /Int. J. Gen. Sys./, v. 14, pp. 331-343

On Bogdonav, an important historical figure in systems science.

Zwick, Martin: (1978) "Fuzziness and Catastrophe", in: /Proc.
of the Int. Conf. of Cyb.andSoc/, pp. 1237-1241, Tokyo/Kyoto

(1978) "Dialectics and Catastrophe", in: /Sociocybernetics/, ed.
F.Geyer et. al., pp. 129-155, Martinus Nijhoff, The Hauge,Neth.

R (1978) "Requisite Variety and the Second Law", in: /Proc. Int.
Conf. of Cyb. and Soc./, pp. 1065-1068, IEEE Sys. Man Cyb.,
Tokyo/Kyoto

Establishes the equivalence of Ashby's Requisite Variety Law and
the second law of thermodynamics.

(1978) "Quantum Measurement and Godel's Proof", /Speculations in
Science and Tech./, v. 1, pp. 135-145

R (1979) "Cusp Catastrophe and Laws of Dialectics", /System and
Nature/, v. 1, pp. 177-187

Expression of dialectical concepts (quantity to quality,
negation, interpenetration of opposites) in terms of catastrophe
theory.

(1982) "Dialectic Thermodynamics", /General Systems Yearbook/, v.
27, pp. 197-204

R (1984) "Information, Constraint, and Meaning", in: /Proceedings
SGSR/, ed. AW Smith, pp. 93-99, Intersystems

Wonderful treatment of the relation between syntax and semantics
in information theory.



# Classic Publications on Complex, Evolving Systems

Author: F. Heylighen
Updated: Mar 28, 1997
Filename: EVOCOPUB.html

The following is a selection of the most cited publications on complex, evolving systems, including work in cybernetics, systems theory, evolutionary biology, self-organization, and complexity studies. Compared to the books and papers on cybernetics and systems, the following list is more up-to-date, puts more emphasis on evolution and self-organization, and includes a number of works which are not usually classified under cybernetics or systems theory. This bibliography is an extension of the one originally condensed out of the references for the Symposium "The Evolution of Complexity"

Each of these books and papers is selected to be both very important to the domain, and of high quality. They are therefore highly recommended for everybody working in the domain. The number of stars (*) denotes the relative importance in terms of the number of citations. For a discussion of the main contributions of the following authors and publications, see my paper "Classic Publications on Complex, Evolving Systems: a citation-based survey" .

The books with links below can be securely ordered and paid for over the web via Amazon.com, the largest bookstore on the Net. Note: although out-of-print books too can be ordered in this way, it may be quicker to try and locate them in a public library.

Anderson P. W., K. J. Arrow, and D. Pines (Eds.). The Economy as an Evolving Complex System, Addison-Wesley, Redwood City CA, 1988. **

Arthur, W. B.: Competing Technologies, Increasing Returns, and Lock-in by Historical Events, The Economic Journal 99: 1989, pp. 106-131. *

Arthur, W. B.: Positive Feedbacks in the Economy, Scientific American, February 1990, pp. 92-99. *

Arthur W. B. Increasing Returns and Path Dependence in the Economy, University of Michigan Press, Ann Arbor, 1994.

Arthur W. B.: Bounded Rationality and Inductive Behavior (the El Farol Problem), American Economic Review 84, pp. 406-411, 1994.

Ashby W. R. An Introduction to Cybernetics, Methuen, London, 1964. **

Ashby W. R. Mechanisms of Intelligence: Writings of Ross Ashby, Intersystems, Salinas CA, 1981.

Ashby, W. R. Design for a Brain - The Origin of Adaptive Behaviour. Chapman and Hall, London, 1960.

Aulin A. The Cybernetic Laws of Social Progress, Pergamon, Oxford, 1982 *

Axelrod R. M. The Evolution of Cooperation, Basic Books, New York, 1984. *

Bak P. and Chen K.: Self-Organized Criticality, Scientific American: January 1991, pp. 46-53.

Bak P., Tang C., & Weisenfeld K.: Self-Organized Criticality. Physical Review A 38: 1988, pp. 364-374. *

Bak P., How Nature Works: The Science of Self-Organized Criticality, Springer, Berlin, 1996.

Bennett C. H. Dissipation, Information, Computational Complexity and the Definition of Organization. Emerging Syntheses in Science, Pines D. (ed.), Addison-Wesley, Redwood City CA, 1985, pp. 215-233. *

Boulding K. E. Ecodynamics: a new theory of societal evolution. Sage, London, 1978.

Campbell, D. T. Evolutionary epistemology. Evolutionary epistemology, rationality, and the sociology of knowledge, G. Radnitzky and W. W. Bartley (eds.), Open Court, La Salle IL, 1987, pp. 47-89.

Campbell, D. T. "Downward Causation" in Hierarchically Organized Biological Systems. Studies in the Philosophy of Biology, F.J. Ayala and T. Dobzhansky (eds), Macmillan, New York, 1974 .

Casti J.L. Complexification: explaining a paradoxical world through the science of surprise, HarperCollins, 1994.

Crutchfield, J., Farmer, J.D., Packard, N., and Shaw, R.: Chaos, Scientific American, 255 (6): December 1986, pp. 46-57.

Darwin C. The origin of species by means of natural selection or the preservation of favoured races in the struggle for life. (Edited with and introduction by J W Burrow). Penguin classics, 1985. (First published by John Murray, 1859) *

Dawkins R. The selfish gene (2nd edition), Oxford University Press, Oxford, 1989. **

Dawkins R. The Extended Phenotype: The Gene as a Unit of Selection, Oxford University Press, Oxford, 1983. *

Dawkins R. The Blind Watchmaker, Longman, London, 1986. *

Eigen M. and P. Schuster. The Hypercycle: A principle of natural self-organization, Springer, Berlin, 1979 **

Eigen M., and R. Winkler-Oswatitsch. Steps Toward Life: a perspective on evolution. Oxford University Press, New York, 1992. *

Fisher R. A. The Genetical Theory of Natural Selection, 2nd edition, Dover Publications, New York, 1958.

Forrester, J. Industrial Dynamics, MIT Press, Cambridge, MA, 1961.

Forrester, J. W. World Dynamics (2nd ed.), Wright-Allen Press, Cambridge, MA, 1973.

Gell-Mann, M., The Quark and the Jaguar: Adventures in the Simple and the Complex, W.H. Freeman, San Francisco, 1994. *

Gleick, J. 1987. Chaos: Making of a New Science, Penguin Books, New York. *

Gould S.J., and N. Eldredge. 1977: Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 3, pp. 115-151.

Haken H. Synergetics, Springer, Berlin, 1978.

Holland J. H. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence, MIT Press, Cambridge MA. ***

Holland J.H. Hidden Order : How Adaptation Builds Complexity , Addison-Wesley 1996.

Holland J. H., Holyoak K. J., Nisbett R. E. & Thagard P. R. 1986 Induction: Processes of Inference, Learning and Discovery, MIT Press, Cambridge MA. *

Jantsch, E., The Self-Organizing Universe: Scientific and Human Implications of the Emerging Paradigm of Evolution, Oxford, Pergamon Press, 1979.*

Kauffman S. A.: Antichaos and Adaptation, Scientific American: August 1991, pp. 78-84 **

Kauffman S. A. The Origins of Order: Self-Organization and Selection in Evolution, Oxford University Press, New York, 1993 ****

Kauffman S. A. At Home in the Universe: The Search for Laws of Self-Organization and Complexity, Oxford University Press, Oxford, 1995.

Langton C. G.: Computation at the Edge of Chaos: phase transitions and emergent computation, Physica D, 42, 1-3, pp. 12-37, 1990. *

Langton, C. G. (Ed.). Artificial Life: The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, Addison-Wesley, Redwood City CA, 1989. **

Langton, C. G., Taylor, C., Farmer, J.D., and Rasmussen, S. (Eds.). Artificial Life II: Proceedings of the Second Artificial Life Workshop, Addison-Wesley, Redwood City CA, 1992. *

Langton, C. G. (ed.), Artificial Life: An Overview, MIT Press, Cambridge, MA, 1995.

Mandelbrot B. B. The Fractal Geometry of Nature, Freeman, New York, 1983.

Maruyama M.: The Second Cybernetics: Deviation-Amplifying Mutual Causal Processes, American Scientist 51, No. 2: 1963, pp. 164-179.

Maturana H. R., & Varela F. J. The Tree of Knowledge: The Biological Roots of Understanding, (rev. ed.), Shambhala, Boston, 1992. ***

Monod, J. Chance and Necessity, Collins, London, 1972.

Nicolis, G, and Prigogine, I. Self-Organization in Non-Equilibrium Systems, Wiley, New York, 1977. **

Nicolis, G. and I. Prigogine. Exploring Complexity, Freeman, New York, 1989.

Prigogine, I. and Stengers, I. Order Out of Chaos, Bantam Books, New York, 1984 ***

Prigogine, I. From Being to Becoming: Time and complexity in the physical sciences, Freeman, San Francisco, 1980.

Ray, T. S. An Approach to the Synthesis of Life. Artificial Life II, C. G. Langton et al. (Eds.), Addison-Wesley, Redwood City CA, 1992, pp. 371-408.

Shannon, C. E., and W. Weaver. The Mathematical Theory of Communication (5th ed.). University of Illinois Press, Chicago, 1963.

Simon, H. A. The Sciences of the Artificial (3rd. edition) MIT Press, Cambridge MA, 1996. **

Thom, R. Structural Stability and Morphogenesis, Benjamin, Reading MA, 1975.

Thompson, D. On Growth and Form, Cambridge University Press, Cambridge, 1917.

Varela, F., Principles of Biological Autonomy, North Holland, New York, 1979.*

von Bertalanffy L. General Systems Theory (Revised Edition), George Braziller, New York, 1973. *

von Foerster H. On self-organising systems and their environments. Self-Organising Systems, M.C. Yovits and S. Cameron (Eds.), Pergamon Press, London, 1960, pp. 30-50. *

von Foerster H. and Zopf, G. (Eds.) Principles of Self-Organization, Pergamon, New York, 1962. *

von Foerster H. Observing Systems: Selected papers of Heinz von Foerster. Intersystems, Seaside, CA, 1981.

von Foerster H. Cybernetics of Cybernetics (2nd edition). Future Systems, Minneapolis, 1996. **

von Neumann J. Theory of Self-Reproducing Automata. (Ed. by A. W. Burks), Univ. of Illinois Press, Champaign, 1966. *

Waldrop M. M. Complexity: The Emerging Science at the Edge of Order and Chaos, Simon & Schuster, New York, 1992. **

Wiener N. Cybernetics: or Control and Communication in the Animal and Machine M.I.T. Press, New York, 1961. **

Wolfram S. Cellular Automata and Complexity: Collected Papers, Addison-Wesley, Reading MA, 1994.

Zeleny M. (Ed.) 1981, Autopoiesis: A Theory of Living Organization, North Holland, New York.

# Cybernetics and Systems Science Compendia

Author: C. Joslyn,
Updated: Jan 1992
Filename: CYBSCOMP.html

One possible use for Principia Cybernetica is the movement towards a detailed encyclopedia of Cybernetics and Systems Science. There are a number of existing dictionaries, encyclopedias, databases, compilations, and source books (both electronically and traditionally published) that are relevant. From the perspective of general knowledge, [ADM52] is an excellent example of a traditional intellectual encyclopedia. It embodies a wealth of semantic and bibliographic richness, detailed development of many great ideas in intellectual history, and connections and cross-references among them. Other traditional encyclopedia projects include [GEWGOS75,KLU88] in mathematics, and [EDP67,FLA79] in philosophy.

In Cybernetics and Systems Science proper, many people have approached the general task of compiling large amounts of useful information. There have been efforts on the part of scholarly organizations, like the International Society for the Systems Sciences (ISSS, formerly the Society for General Systems Research (SGSR)), to develop lists of terms and concepts held in common with Cybernetics and Systems Science. There are bibliographies of systems literature [TRRHOW84], at least four dictionaries of cybernetic terms [AM84,FRC91,KRK84,MUA68], and one encyclopedia of Systems Science [SIM87]. An important contribution is the large pamphlet Education in the Systems Sciences [SNB90], detailing a great deal of information about the whole nature of Cybernetics and Systems Science and how it is carried out around the world. [CLB84] includes a substantive glossary of systems terms. We should note the GENSYS project, directed by Len Troncale, which has an ambition similar to Principia Cybernetica.

## Bibliography of Dictionaries, Encyclopedias, Glossaries, Principiae, Manifestos, Textbooks, Histories, Sourcebooks, and Others Related to Cybernetics and Systems

• [ABR88] Abraham, Ralph: (1988) "Mathematics and Evolution: A Manifesto", World Futures, v. 23, pp. 237-261

• [ADM52] Adler, MJ: (1952) Synopticon of Great Books, Encyclopedia Brit., Chicago

• [AM84] American Society for Cybernetics: (1984) Glossary on Cybernetics and Systems Theory, ASC, GWU, Washington

• [ASR56] Ashby, Ross: (1956) Introduction to Cybernetics, Methuen, London

{A classic; still the finest introductory book on cybernetics and systems theory. Includes problem sets, examples, and diagrams.}

• [ATMBAF89] Atkinson, M, and Bancilhon, F, et al.: (1989) "Object-Oriented Database System Manifesto", in: Proc. Deductive & OO Databases, Kyoto

• [BIPDAM] Bishop, Peter, and Darton, Michael eds.: Enyclopedia of the World's Faiths

• [BOA80] Bogdanov, A.: (1980) Essays in Tektology, Intersystems

{Translation of historical foundation of systems science.}

• [BRSE] Brohnstein and Semendyave: Handbook of Mathematics

• [BRG72] Brown, GS: (1972) Laws of Form, Julian Press, New York

{Philosophy of and notational system for propositional logic. Basis for a whole school of graphical approaches to classical logic.}

• [CAR79a] Cavallo, Roger E: (1979) "Systems Research Movement", SGSR Bulletin, v. 9:3

{Primer on Systems Science.}

• [CHM88] Char, MBS: (1988) "Cybernetic Principle", Cybernetica, v. 31:2, pp. 97-98

• [CLB84] Clemson, Barry: (1984) Cybernetics: A New Management Tool, Abacus Press, Kent

{Guide to the theory and practice of management cybernetics, based on Beer. Includes extensive glossary.}

• [COJ77b] Cornacchio, Joseph V: (1977) "Systems Complexity: A Bibliography", Int. J. Gen. Sys., v. 3, pp. 267-271

• [DET59] De Chardin, Teilhard: (1959) Phenomenon of Man, Harper and Row, New York

{Early systemic evolutionary theology.}

• [EDP67] Edwards, Paul ed.: (1967) Encyclopedia of Philosophy, v. 1-8, MacMillan, New York

• EN] : Images Encyclopedia, Enterprise Publisher, Des Plaines IL

{Computer database encyclopedia of iconic symbols.}

• [FLA79] Flew, Antony ed.: (1979) Dictionary of Philosophy, Pan Books, London

• [FRC91] Franc cois, C: (1991) General Systems Theory and Cybernetics Dictionary

• [GEWGOS75] Gellert, W, and Gottwald, S et al. eds.: (1975) Concise Encyclopedia of Mathematics, Van Nostrand, New York

• [GLV66] Glushkov, VM: (1966) Introduction to Cybernetics, Academic Press, New York

{Excellent book on cybernetics, translated from Russian.}

• [KLG69] Klir, George: (1969) Approach to General Systems Theory, van Nostrand, New York

{An early book that describes the nucleus of what is known now as the General Systems Problem Solver: a general framework, or language, for systems science.}

• [KLG91a] Klir, George: (1991) Facets of Systems Science, Plenum, New York

{Textbook and essay collection for a first Systems Science course.}

• [KLU88] : (1988) Encyclopedia of Mathematics, Kluwer, Hingham MA

• [KRK86] Krippendorf, Klaus: (1986) Dictionary of Cybernetics, available through ASC

• [LAA76] Lacey, AR: (1976) Dictionary of Philosophy, Routledge, London

• [MIHQUE85] Miser, HJ, and Quade, ES eds.: (1985) Handbook of Systems Analysis, North-Holland, New York

• [MOG62] Moore, GE: (1962) Principia Ethica, Cambridge U Press, Cambridge

• [MUA68] Müller, A ed.: (1968) Encyclopedia of Cybernetics, Manchester U. Press, Manchester UK

{Translated from German by G. Gilbertson.}

• [NEOCAR38] Neurath, Otto, and Carnap, R et. al.: (1938) International Encyclopedia of Unified Science, U. Chicago, Chicago

• [PAJ88] Palmer, James D: (1988) "Review: Systems & Control Encyclopedia, edited by MG Singh", IEEE Trans. on Sys., Man and Cyb., v. 18:2, pp. 33

• [PAV72] Parsegian, VL: (1972) This Cybernetic World, Doubleday, Garden City, NY

{Survey of cybernetics.}

• [PAG61] Pask, Gordon: (1961) Approach to Cybernetics, Harper & Brothers, New York

• [RAA86] Rapoport, Anatol: (1986) General Systems Theory: Essential Concepts & Applications, Abacus, Cambridge

• [SIM87] Singh, MG ed.: (1987) Systems and Control Encyclopedia, Pergamon, Oxford

{Eight volumes, from the perspective of technical systems and control theory; also many excellent articles of a more general methodological and philosophical nature.}

• [SNB90] Snow, Blain: (1990) Education in the Systems Sciences, Elmwood Institute, Berkeley CA

{1st edition, Winter 1990, PO Box 5805, Berkeley CA 94705 USA. "Annotated Guide to Education and Research Opportunities in the Sciences of Complexity": degree-granting programs of study; societies, associations and organizations; directory of researchers; publications: periodicals and books; bibliography; keyword and cluster index; name index.}

• [TRR83] Trappl, Robert ed.: (1983) Cybernetics: Theory and Applications, Hemisphere, Washington

{Anthology of foundations of systems and cybernetics; review of applications; complete bibliography. Beer, Atlan, Pichler, Klir, Pask, Nowakowska, Arbib, Laszlo.}

• [TRRHOW84] Trappl, Robert, and Horn, W, et. al. eds.: (1984) Basic and Applied General Systems Research, Int. Fed. Sys. Res., Laxenburg, Austria,

{Bibliography from 1977-1984, Continuation of earlier bibliography compiled by Klir.}

• [VOH49] von Foerster, Heinz ed.: (1949+) Transactions of the Macy Conferences on Cybernetics, v. 1-10, Josiah Macy Found., New York

• [WEG75] Weinberg, Gerard M.: (1975) Introduction to General Systems Thinking, Wiley, New York

{Excellent popular introduction to systems theories and principles.}

• [WHARUB10] Whitehead, Alfred, and Russell, Bertrand: (1910) Principia Mathematica, v. 1-3, Cambridge U Press

• [WHARUB62] Whitehead, Alfred, and Russell, Bertrand: (1962) Principia Mathematica to *56, Cambridge U Press

• [WI79] : (1979) Citation Indexes, Wiley, New York

• [WIN48] Wiener, Norbert: (1948) Cybernetics, MIT Press, Cambridge

{First book on cybernetics from this master of the field.}

• [ZUW89] Zurek, Wojciech: (1989) "Complexity, Entropy & the Physics of Information: A Manifesto", Sante Fe Institue, v. 4:1, pp. 26-29

# Cybernetics and Systems Journals

Author: F. Heylighen, C. Joslyn,
Updated: Mar 3, 1998
Filename: JOURNALS.html

The following is an alphabetical list of journals (and newsletters) on, or related to, cybernetics and systems research. A "*" denotes the journals that are most central to the domain. Some of the addresses may no longer be up to date, though most material is fairly recent. Please send a note to PCP@vub.ac.be or annotate this page if you would like to make an addition or correction. See also the list of cybernetics journals from the ASC, and ASSA's list of Systems Journals.

Editorial address: Jean-Arcady Meyer, Editor, Adaptive Behavior, Groupe de BioInformatique, Ecole Normale Superieure, 46 rue d'Ulm, 75230 Paris Cedex05, FRANCE.
Phone: (1) 43 29 12 25 ext 3623
Fax: (1) 43 29 70 85
Email: meyer@wotan.ens.fr, meyer@frulm63.bitnet.
Publisher: MIT Press

Comments: devoted to experimental and theoretical research on adaptive behavior in animals and in autonomous artificial systems, with emphasis on mechanisms, organizational principles, and architectures that can be expressed in computational, physical, or mathematical models.; emphasizes an approach complementary to traditional AI, in which basic abilities that allow animals to survive, or robots to perform their mission in unpredictable environments, will be studied in preference to more elaborated and human-specific abilities; explicitly takes into account the environmental feedback.

## Artificial Life

Editorial address: Christopher G. Langton, Santa Fe Institute, 1160 Old Pecos Trail, Suite A, Santa Fe NM 87501-4768, USA.
Publisher: MIT Press

## Behavioral and Brain Sciences

an international journal of current research and theory with open peer commentary
Editorial address: Stevan Harnad (ed.), 20 Nassau St., Suite 240, Princeton NJ 08542, USA.
Phone: 609-921-7771
Publisher: Cambridge University Press

Comments: Interdisciplinary journal on theoretical and experimental psychology, with emphasis on cognitive and evolutionary models. Dialog format, not afraid to deal with philosophical and theoretical issues. To be considered as a commentator , to suggest other appropriate commentators, or for information about how to become a BBS Associate, please send email to:, harnad@clarity.princeton.edu

## *Behavioral Science

Journal of the Int. Society for Systems Science
Editorial address: Warren Froelich (managing editor), PO Box 8369, La Jolla CA. 92038-8369, USA.
Fax: 00 - 1- 619 - 456 01 97
Email: millerj@sdsc.bitnet
Publisher: ISSS

Comments: Old journal of the Society for General Systems Research (presently ISSS). Mostly social and psychological systems theory, often withe reference to Miller's living systems theory. Emphasis on interdisciplinarity, and generalizability of results across levels. Used to be included in many citation and reference indexes as an important journal, but has gone downhill and seems to have difficulty surviving. Co-sponsored by the Inst. of Management Sciences

## Biological Cybernetics

Editorial address: G. Hauske (ed. in chief), Lehrstuhl fuer Nachrichtentechnik, Technische Universitaet, Arcisstrasse 21, D-80290 Muenchen, Germany
Publisher: Springer

## BioSystems

Editorial address: ed. AW Schwartz, Box 85, Limerick, Ireland.
Publisher: Elsevier (North Holland)

## Cognitive Systems

(European Society for the Study of Cognitive Systems)
Editorial address: Dr. G.J. Dalenoort, Dept. of Psychology, Univ. of Groningen, P.O.Box 41 096, 9701 CB Groningen, The Netherlands, Tel : +31-50-3636448 / 3636454 (or3636472), Fax : +31-50-3636304,

rather small journal, locally published, with systems inspired work on cognitive science and neural networks

## Complexity

Comments: New "magazine-like" journal close to the Santa Fe Institute. Edited by John Casti and Harold Morowitz. Contains mostly introductory and survey articles.

## Complexity International

Comments: Australian web journal on complex systems research

## Complex Systems

a journal devoted to ...research... of systems with simple components but complex overall behavior
Editorial address: (editorial office) [old address], Center for Complex Systems research, University of Illinois at Urbana-Champaign, 508 South Sixth Street, Champaign IL 61820 USA.
Email: jcs@complex.ccsr.uiuc.edu
Publisher: Complex systems Inc.

Comments: Physics journal on dynamic systems theory, complex systems theory, cellular automata and networks, etc.

## Cybernetica

(Association Internationale de Cybernétique)
Editorial address: Palais des Expositions, Place André Rijckmans, B-5000 Namur , Belgium.
Phone: 081-73 52 09.
Fax: 32 - 81 - 74.29.45, 081 - 23 09 45
Email: CYB@INFO.FUNDP.AC.BE.

Comments: rather small, locally published, but long standing journal on cybernetics

## Cybernetics & Human Knowing

Editorial address: Soren Brier, Royal School of Librarianship, Langagervey 4, DK-9220 Aalborg Ost, Denmark.
Phone: +45-98-157922
Fax: +45-98-151042
Publisher: Soren Brier

Comments: emphasis on second-order cybernetics and semiotics (board: von Glasersfeldt, Von Foerster, Luhmann, Maturana, Braten, etc.)

## Cybernetics and Systems Analysis

(formerly 'cybernetics', translation of the Russian "Kibernetika")
Editorial address: V.S. Mikhalevich (ed.), V.M. Gluchkov Institute of Cybernetics, Ukrainian Academy of Sciences, Kiev.
Publisher: Plenum

## *Cybernetics and Systems

An International Journal
Editorial address: c/o R. Trappl (editor), Dep. of Medical Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6, A-1010 Wien, Austria.
Email: robert@ai.univie.ac.at
Publisher: Taylor & Francis

Comments: Excellent technical journal on all aspects of systems theory and cybernetics.

## Emergence: A Journal of Complexity Issues in Organizations and Management

Publisher: New England Complex Systems Institute

## Evolution and Cognition

Email: sec @kla.univie.ac.at

Comments: interdisciplinary research on evolutionary epistemology and evolutionary systems

## Evolution of Communication

Comments: origins of human language, but also the evolutionary continuum of communication in general

## General Systems Yearbook

Editorial address: Howard T. Odum, University of Florida, Dept of Environmental Engineering, Gainesville FL 32611, USA.
Publisher: ISSS

Comments: For years the annual publication of the Int. Soc. for Gen. Sys. Res. (ISGSR, now ISSS), featuring selected publications by foundational authors. Address probably out-of-date.

## Grundlagenstudien aus Kybernetik und Geisteswissenschaften

Publisher: Gunter Narr Verlag

## Human Systems Management

Editorial address: Prof. Milan Zeleny (ex. editor), Graduate School of Business Administration, Fordham Univ. at Lincoln Center, CBA-626-E New York, NY 10023, U.S.A.
Publisher: IOS, Amsterdam

## IEEE Transactions on Control Systems Technology

Editorial address: Bruce H. Krogh, Editor, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213-3890, USA, Sebastian Engell, Co-Editor, FB Chemietechnik, University of Dortmund, Postfach 50 05 00, D-4600 Dortmund 50, GERMANY.
Phone: 412 268 2472
Fax: 412 268 3890
Email: krogh@galley.ece.cmu.edu

Comments: new developments in all areas of control systems technology, including, but not limited to, new sensor and actuator technologies, software and hardware for real-time computing and signal processing in, control systems, tools for computer-aided design of control systems, new approaches to control system design and implementation, experimental results, distributed architectures, intelligent control, and novel applications of control engineering methods.

## IEEE Transactions on Systems, Man and Cybernetics

Editorial address: Andrew P. Sage (editor), George Mason University, 4400 University drive, Fairfax, VA 22030, USA.
Phone: (703)-323-2939
Publisher: IEEE

Comments: Long-standing cybernetics journal from a "reputable" publisher. More towards technical systems science, still broad-minded

Editorial address: Prof.Dr. Gerhard Chroust, International Federation for Systems research, c/o Systemtechnik und Automation, Kepler University Linz, A-4040 Linz, Austria.
Email: GC@shannon.sea.uni-linz.ac.at
Publisher: International Federation for Systems Research

## *International Journal of General Systems

Editorial address: G.J. Klir, Department of Systems Science, Thomas J. Watson School, State University of New York, Binghampton, NY 139001, USA.
Email: TASTLE@BINGVAXA.BITNET (William Tastle, Associate Editor IJGS)
Publisher: Gordon and Breach

Comments: Another premiere systems science journa. Frequently technical, otherwise broad. Complexity theory, mathematical systems theory, philosophy, history.

## International Journal of Systems Science

Editorial address: Prof. B. Porter, Dep. of Aeronautical and Mechanical Engineering, University of Salford, UK.
Publisher: Taylor & Francis

## Journal of Applied Systems Analysis

Editorial address: Peter Checkland, Dep. of Systems and Information Management, University of Lancaster, Bailrigg Lancaster LA1 4YX, UK.
Publisher: University of Lancaster

Comments: major focus of work developing and using Soft Systems Methodology

## Journal of Biological Systems

Editorial address: P.M. Auger, Biomathematiques, Dept. d' Ecologie, Faculté des Sciences, Université de Bourgogne, Batiment "Mirande", BP 138, 21004 Dijon Cedex, France.
Publisher: World Scientific

## Journal of Complexity

Editorial address: Joseph F. Traub (ed.), Computer Science department, Columbia University, New York City NY 10027.

Comments: emphasis on mathematical and computational complexity.

## Journal of Complex Systems

Editorial address: Eric Bonabeau, Santa Fe Institute
1399 Hyde Park Rd., Santa Fe, New Mexico 87501, USA,
Email: bonabeau@santafe.edu.
Publisher: Hermes, Paris

Comments: new interdisciplinary journal in the complex adaptive systems tradition, with PCP editor Cliff Joslyn in its editorial board.

## Journal of Intelligent & Robotic Systems

Theory and applications
Editorial address: S. G. Tzafestas (ed.), National Technical. Univ., Div. of Computer Sc., Dep. of Electrical Eng., 15773 Zographou, Athens, Greece.
Publisher: Kluwer

## Journal of Memetics - Evolutionary Models of Information Transmission

Comments:the first peer-reviewed journal on memes, freely available on the Web, sponsored by the Principia Cybernetica Project.

## Journal of Social and Evolutionary Systems

formerly: Journal of Social and Biological Structures
Editorial address: Dr. Paul Levinson (Editor), Connected Education, Inc., 65 Shirley Lane, White Plains, NY 10607, USA.
Phone: 914-428-8766
Email: PLevinson@cinti.com

## Journal of Systems Engineering

Editorial address: Prof. D.T. Pham, University of Wales, School of Electrical, Electronic and Systems Engineering., P.O. Box 904, Cardiff CF1 3YH, United Kingdom.
Phone: 0222- 874429, Telex 497368
Fax: 0222- 874192
Email: PhamDT@cardiff.ac.uk

## *Kybernetes

the International Journal of Cybernetics and General Systems
Editorial address: B.H. Rudall (ed.), Craig yr Halen, Menai Bridge, Gwynedd LL59 5HD, UK.
Phone: (0248) 71 26 36
Publisher: MCB University Press

## Kybernetyka

Editorial address: Academy of Sciences of the Ukrainian SSR, 252207 Kiev 207, Ukraine.

## Mathematical Systems Theory

an International Journal of mathematical computing theory
Editorial address: A.L. Rosenberg (ed.), Comp. and inf. Sc., Univ. Massachusetts, Amherst MA 01003, USA.
Publisher: Springer Verlag NY

## Open Systems & Information Dynamics

Publisher: Kluwer

Comments:interdisciplinary research in mathematics, physics, engineering and life sciences; system and information-theoretic approaches dealing with control, filtering, communication, pattern recognition, chaotic dynamics, memory and cooperative behaviour in open complex systems

## *Revue Internationale de Systémique

Editorial address: c/o B. Paulré, Collège de systémique de l'AFCET, 156 Boulevard Péreire, F-75017 Paris, France.
Publisher: Dunod

Comments: journal of the French systems community; articles in French and English.

## Sistemica

Journal of the inter and transdisciplinary management of complexity
Editorial address: R.A. Rodrigues-Ulloa, Andean Institute of Systems -IAS, PO Box 18-0680 , Lima 18, Peru.
Publisher: IAS

Comments: In English and Spanish, Latin American Journal of systems theory .

## Social Systems

Editorial address: Johannes Schmidt, Fakultät für Soziologie, Universität Bielefeld, Postfach 100 131, D - 33501 Bielefeld,
Tel.:49-(0)521-106-4623 / 3998, Fax: 49-(0)521-106-6020.
Email: Soziale.Systeme@post.uni-bielefeld.de
Publisher: Leske & Budrich

Comments: In German, English and French; systems theoretical work in sociology, applications of general systems concepts (inc. cognition and difference) to society, interaction and organization .

## System Dynamics Review

System Dynamics Society
Editorial address: Julia S. Pugh, Executive Director, 49 Bedford Road, Lincoln, MA 01772, USA.

Comments: Applications of dynamical theory to social systems modeling.

## Systeme

Interdisciplinäre Zeitschrift für systemtheoretisch orientierte Forschung und Praxis in den Humanwissenschafte
Editorial address: Systeme, Postfach 171, A-1013 Wien, Austria
Publisher: Österreichische Arbeitsgemeinschaft für System Therapie und System Studien, ÖAS - Verlag, Leopoldsgasse 51/5, A-1020 Wien, Austria

## Systems & Control Letters

Editorial address: J.C. Willems (managing editor), Mathematics Institute, University of Groningen, PO Box 800, 9700 AV Groningen, Nederland, mailing address: Zandsteenlaan 16, 9743 TN Groningen, Nederland.
Publisher: North Holland

## Systemic Practice and Action Research

(formerly Systems Practice ) Editorial address: Prof. Robert Flood, Department of Management Systems and Sciences, University of Hull, Hull HU6 7RX, UK.
Publisher: Plenum

Comments: application of "critical systems thinking" to improving work and organization

## *Systems Research

official journal of the international federation for systems research
Editorial address: Prof. Michael C. Jackson. Dean of Computing and Information Systems, University of Humberside, Marvell Hall, Hull, HU6 7RT, UK .
Phone: +1482-440550 Ext. 3720
Fax: +1482-445715
Publisher: Wiley

Comments: another central journal for the systems theory community; emphasis on social science, management and systems thinking, less on mathematics and technology

## Systems Research and Information Science

Editorial address: L. Johnson, Computing Laboratory, University of Kent at Canterbury, Kent CT2 7PE, UK.
Publisher: Gordon and Breach

## Systems Science

Editorial address: Prof. Zdzislaw Bubnicki (editor), Institute of Control and Systems Engineering, Wroclaw Technical University, 50-372 Wroclaw, ul. Janizewskiego 11/17, Poland.
Publisher: OR RAN, PKin, 00-901 Warszawa, Poland.
Comment: general systems theory, theoretical problems of analysis, modelling, design and control of systems (with particular reference to large-scale systems), and their applications to industrial, information, management, socio-economic and biological systems.

## The newsletter : American Society for Cybernetics

Editorial address: Frederic Steier or Barry Clemson (editors), Center for Cybernetic Studies in Complex Systems, Old Dominion University, Norfolk, VA 23529, USA.
Phone: (804) 683 4558
Publisher: American Society for Cybernetics

## World Futures: the Journal of General Evolution

Editorial address: Maria Sagi (Managing editor), Uri u.49, H-1014 Budapest, Hungary.
Fax/tel: 36-1-156 9457,
Publisher: Gordon and Breach

Comments: Edited by Ervin Laszlo, board includes Ralph Abraham, Bob Artigiani, Bela Banathy, Peter Allen, George Kampis, Vilmos Csanyi, Varela. Contents: general patterns of change and development in nature as well as society; articles, short communications, and book reviews relating to evolutionary processes in all fields of science, with special attention to multidisciplinary approaches.

# Cybernetics and Systems Societies

Author: F. Heylighen
Updated: Jul 24, 1998
Filename: SOCIETIES.html

The following national and international societies and organizations are concerned with research related to cybernetics and systems science. A "*" denotes the organizations that are most central to the domain. Some of the addresses may no longer be up to date, though most material is fairly recent. Please send a note to fheyligh@vnet3.vub.ac.be or annotate this page if you would like to make an addition or correction.

### * American Society for Cybernetics

c/o Center for Social and Organizational Learning, Department of Management Science, George Washington University, Washington, DC 20052, USA.
Tel: 202-994-5203 Fax: 202-994-5225
E-mail: ASC@gwis2.circ.gwu.edu

### *Association Argentina de Teoria General de Sistemas y Cybernetica

Address: Buenos Aires C.C. 33 1641 Acassuso, Buenos Aires, Argentina,
Phone: 792 - 7160

### Associacion Mexicana de Sistemas y Cybernetica, a.c.

Address: Dr. J.L. Elohim (President), Antonio Sola 45, Col. Condesa, C.P. 06140, Mexico D.F.. (not up-to-date)

### Association F. Gonseth

Institut de la Méthode
Address: C.P. 1081, CH-2501 Bienne, Switzerland.
Phone: 0041 32 23 83 20
E-mail : logma@access.ch (N. Peguiron)

### * Association Internationale de Cybernétique

International Association for Cybernetics
Address: Palais des Expositions, Place André Rijckmans, B-5000 Namur , Belgium.
Email: CYB@INFO.FUNDP.AC.BE

### Behavioral Systems Science Organization

Address: P.O. Box 2051, Falls Church, Virginia 22042, USA.

### BIRA

Belgian Institute for Automatic Control
Phone: (03) 216 09 96

### Cambridge Cybernetic Society

Phone: 617-489-9500

### Cognitive Science Society Inc.

Address: Dr. Alan Lesgold (secretary/treasurer), 516 Learning Research and Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburg PA 15260, USA.

### * Collège de Systémique de l' Association Française pour la Cybernétique économique et Technique (AFCET)

Address: Prof. R. Vallée , 156 Boulevard Péreire , 75017 Paris, France.
Phone: 00 33 -1- 42 67 93 12

Address: Cesar Buda, Via Larga 11, I-20122 Milano, Italia.

### * Deutsche Gesellschaft für Systemforschung e.V.

Address: DGSF, c/o Michaela Hammer, Burgstr. 6, D- 03046 Cottbus, Germany.
Phone: 0355-3 16 16, Fax: 0355-3 16 26
Email: korn@rz.tu-cottbus.de

### European Society for the Study of Cognitive Systems

Address: Gerhard Dalenoort, Instituut voor Experimentele Psychologie, Rijksuniversiteit Groningen, Postbus 14, NL- 9750 AA Haren, Nederland.

### Gesellschaft fuer Wirtschafts- und Sozialkybernetik (GWS)

Address: Prof. Dr. Bernd Schiemenz (director general), Philipps-Universitaet Marburg, Fachbereich Wirtschaftswissenschaften, BWL I: ABWL und Industriebetriebslehre, Am Plan 2, D-35032 Marburg, Germany
Phone:+49 - 6421 - 28 17 18
Fax:+49 - 6421 - 28 89 58
E-Mail:
schiemen@wiwi.uni-marburg.de

### Greek Systems Society

Address: Dr. Michael Decleris (managing director), 82 Fokionis Negri Street, Athens 11361, Greece.

### Human Behavior and Evolution Society

Address: Margo Wilson, Dep. of Psychology, McMaster University, Hamilton, ONtario, L8S 4K1, Canada.
Fax: (416) 529-622 25
Email: wilson@mcmaster.ca

### ICS, Institut fuer Kybernetik und Systemtheorie

Address: Am Huelsenbusch 54, D-44803 Bochum,
Phone:0049 (0 23 02) 80 20 15
Email: webmaster@xpertnet.de

### IEEE Systems, Man and Cybernetics Society

Address: Andrew P. Sage, George Mason University, 440 University drive, Fairfax, VA 22030, USA.

### IFAC

International Federation for Automatic Control
Address: Schlossplatz 12, A-2361 Laxenburg, Austria.

### * IFSR

International Federation for Systems Research

Address: Prof.Dr. Gerhard Chroust (secretary), International Federation for Systems research, c/o Systemtechnik und Automation, Kepler University Linz, A-4040 Linz, Austria.
Email: GC@shannon.sea.uni-linz.ac.at

### IFORS (Internation Federation for Operational Research Societies)

Address: Mrs. H. Welling (secretary), c/o IMSOR, Building 321, Technical University, DK-2800 Lyngby, Denmark.
Phone: 45 - 42 - 88 22 22 ext. 4410

### IIASA

International Institute for Applied Systems Analysis

Address: Schlossplatz 12, A-2361 Laxenburg, AUSTRIA.
Gopher server

### IIIS: International Institute of Informatics and Systemics

Address: 14269 Lord Barclay Dr., Orlando Florida 32837, USA.
Email: iiis@aol.com

### Instituto Mexicano de Sistemas

Address: Javier Marquez d. , Reforma 199 Piso 14 [old address], Col. Cuauhtemoc, Mexico 06500 D.F.
Phone: FAX: 664-22-14

### Internat. Research Laboratory

Program Systems Institute of the USSR Academy of Sciences

### * International Society for the Systems Sciences (ISSS)

Address: Prof. J.D.R. de Raadt (managing director), College of Business, Box 8793, Idaho State University, Pocatello, Idaho 83209, USA.Int. Business Office, Dr. Harold Nelson, Antioch University, 2607 2nd Ave., Seattle, Washington 98121-1211, USA.
Phone: 208 233 6521 (de Raadt), (213) 743-2411
Fax: 208 236 43 67
Email: derajdon@ba.isu.edu (de Raadt), or peenolp@aol.com ( Linda Peeno)

### Istituto di Cibernetica

Address: Dr. F. Ventriglia, Istituto di Cibernetica, Via Toiano 6, 80072 - Arco Felice (NA), Italy.
Phone: (39-) 81-8534 138
Fax: (39-) 81-5267 654
Email: LC4A@ICINECA.bitnetComments: An International School on Neural Modelling and Neural Networks was organized under the sponsorship of the Italian Group of Cybernetics and Biophysics of the CNR, the Institute of Cybernetics of the CNR and the National Committee for Physics of the CNR; co-sponsor the American Society for Mathematical Biology. Tx 710483

### Konrad Lorenz Institute for Evolution and Cognition Research

Email: sec @kla.univie.ac.at Comments: interdisciplinary research on evolutionary epistemology and evolutionary systems. Publishes the journal "Evolution and Cognition"

### * Oesterreiches Studiengesellschaft fuer Kybernetik

(Austrian Society for Cybernetics)
Address: Prof. Robert trappl (President), Schottengasse 3, A-1010 Wien, Austria.
Email: sec@ai.univie.ac.at

### Polskie Towarzystwo Cybernetyczne

(Polish Cybernetical Society)
Address: Prof. Dr. W. Gasparski, Design Methodology Unit, Dep. of Praxiology, Polish Academy of Sciences, Nowy Swiat Str. 72, 00-330 Warsaw, Poland.

### Santa Fe Institute

Address: 1120 Canyon Rd., Santa Fe NM 87501, USA.
Phone: (505) 984-8800
Fax:: (505) 982-0565
Email: ars@santafe.edu
Comments: internationally famous interdisciplinary research center for the sciences of complexity

### Sociée Suisse de Systémique

Swiss Systems Society

### * Systeemgroep Nederland

Address: Dr. K.A. Soudyn, Katholieke Hogeschool Tilburg, Hogeschoollaan 225, Tilburg, Nederland.

### * The Cybernetics Society (UK)

Address: Dr. Brian Warburton (Chairman), 37A Oatlands Avenue, Weybridge, Surrey, KT13 9SS UK. .
Phone:+44 1932 850649
Email: brwarburto@aol.com

### The Elmswood Institute

Address: PO Box 5805, Berkeley CA 94705, USA.

### The Gaia Institute

Address: Cathedral of St. John the Divine, 1047 Amsterdam Ave. at 112th St., New york, NY 10025.
Phone: 212 - 295 1930

### The International Institute for Advanced Studies in Systems Research and Cybernetics

Address: Prof. George E. Lasker, University of Windsor, School of Computing Science, Windsor, Ontario, Canada N9B 3P4.

### The Society for the Study of AI and Simulation of Behaviour

Address: Judith Dennison, Cognitive Studies Programme, Arts Building, University of Sussex, Brighton BN1 9QN, UK.

### The Society of Management Science and Applied Cybernetics (SMSAC)

Address: Prof. Dr. A. Ghosal (secretary), O.R. Unit, C.S.I.R. Complex, , N.P.L. Campus, New Delhi 110012, India.

### The University of the World

Address: 1055 Torrey Pines Road, Suite 203, La Jolla CA 92037 USA.
Phone: 619 - 456 01 03
Fax: 619 - 456 01 97
Email: MILLERJ@SDSC.BITNET (James Miller)

### Working Group on Sociocybernetics and Social Systems

(International Sociological Organization)
Address: Felix Geyer (secretary), SISWO, Plantage Muidergracht 4, 1018 TV Amsterdam, The Netherlands.
Fax: 31 20 622 9430
Email: geyer@SISWO.UVA.NL

### * Union Européenne de Systémique

Address: c/o AFCET, 156 Blvd. Péreire, F-75017 Paris, France.

### * United Kingdom Systems society

Address: United Kingdom Systems Society Professor Michael C Jackson, Dean of the School of Computing and Information Systems, University of Humberside, Hull HU6 7RT, United Kingdom.

### Washington Evolutionary Systems Society

Address: Robert Crosby (secretary), 646 E. Capitol St. NE, Washington DC 20003, USA.
Phone: (202) 547 4701
Fax:: (202) 543 8393
BBS: WESSNET: (703) 739 0688

### WISINET

* Executive Group of Worldwide International Systems Institutions Network
Address: Dr. Istvan Kiss (secretary), POB 446, Budapest , Hungary H-1536.

### * World Organization of Systems and Cybernetics

(WOSC)
Addr ess: Prof. Robert Vallée (president), 2 Rue de Vouillé, F-75015 Paris, France.
Phone: 532-727, 530-214

# IAC - International Association for Cybernetics

Author: Jean Ramaekers
Updated: Mar 14, 1994
Filename: IAC.html

The International Association for Cybernetics was incorporated at Namur, on January 6th, 1957. The idea to incorporating an International Association was born following to the 1st International Congress on Cybernetics held in 1956. Its tenth anniversary was commemorated in 1967 in presence of its Majesty the King of Belgium. Since 1971, it is recognized by the UNESCO as a nongovernmental organization of mutual information.

Aims of the Association:

• - to ensure a permanent and organized connection between researchers whose work in various countries is related to different sectors of Cybernetics,
• - to promote the development of this science and of its technical applications as well as the dissemination of results obtained in this field.

The Association has developed and is still developing the following activities:

• - Organization of international congresses on cybernetics and publication of the proceedings relevant to these congresses.
• Since 1956, 14 congresses have been organized and all were held at Namur.
• - Organization of colloquia, meetings and symposia dedicated to various subjects relating to Cybernetics or collaboration to the organization of similar manifestations.
• - Contacts with people and organizations involved in Cybernetics:
1. by means of our review CYBERNETICA and our quarterly NEWSLETTER;
2. through Affiliated Associations;
3. members are called upon to represent the Association on various congresses.
• - Spreading information related to the development and application of Cybernetics.

President: Prof. Jean Ramaekers (Belgium),

Secretariat: International Association for Cybernetics
Palais des Expositions,
Place Andre Rijkmans,
5000 Namur (Belgium)

# Mailing Lists and Newsgroups on Cybernetics and Systems

Author: F. Heylighen, C. Joslyn
Updated: Nov 28, 1997
Filename: CSMAIL.html

The following is an (incomplete) list of electronic discussion forums on cybernetics and systems science. Please annotate this page, or send us an email if you want to add a forum, or if you find some information to be out of date.

For mailing lists, the first address mentioned is the address to which you should send mail if you want it distributed to all subscribers (for "closed" lists, such as PRNCYB-L this is only possible if you are a subscriber yourself). The subscription address is the one where you should send mail to subscribe, unsubscribe or perform other administrative commands. The maintainer address is the one of the person who is responsible for administering the list, and where you might send questions if the automatic subscription procedures somehow don't work.

There are basically three types of mailing lists:

• The first type is run manually. These are identified by a subscription address that looks like listname-request@somewhere.somedomain. To subscribe, send a message to that address with some text a human being can understand, e.g. "Please subscribe me to .... list."

• To subscribe to a majordomo list you'll need to send the line
subscribe LISTNAME
in the body of the message to the subscription address. LISTNAME is the first part (before the "@") of the list address, e.g. PRNCYB-L.

• To subscribe to a LISTSERV list you need to send the message
SUBSCRIBE LISTNAME Your_First_Name Your_Last_Name
in the body to the subscription (LISTSERV) address. Some hosts run a similar piece of software called listproc obeying similar commands.
For more details on lists in general: see Directory of Scholarly E-Conferences

## Principia Cybernetica Mailing list

PRNCYB-L@BINGVMB.CC.BINGHAMTON.EDU
LISTSERV@BINGVMB.CC.BINGHAMTON.EDU
Maintainer:
cjoslyn@bingsuns.cc.binghamton.edu (Cliff Joslyn)
Associated server:
Principia Cybernetica Web
Official mailing list (closed) of the Principia Cybernetica Project. See the description of PRNCYB-L for information on how to subscribe.

## Cybernetics Discussion Group

CYBCOM@HERMES.CIRC.GWU.EDU
listserv@gwuvm.gwu.edu
Maintainer:
Philip Wirtz
Associated server:
Listservs at the CSOL
CYBCOM is presently the list which most generally addresses the domain of cybernetics and systems theory, taking over from the terminated CYBSYS-L (see below). CYBCOM stands for the "CYBernetic COMmunications Group" over the Internet. It is based at the George Washington University in Washington, D.C., USA. CYBCOM, was established in Fall 1993. The Center for Social and Organizational Learning at the GWU invited a group of people to form a "steering committee" for the CYBCOM List, to facilitate future discussions. This is an effort to make sure that, whenever one of the committee members is not available for some reason, conversations on the list can still be facilitated and kept going. Currently, Dr. Stuart Umpleby (Director of the Center for Social and Organizational Learning, GWU), Dr. Paul Pangaro (President, PANGARO INC., Boston), Dr. Sanaullah Kirmani (Visiting Professor of Management Science, GWU), and Jixuan-Hu are on the committee.

## Control Systems Group List (CSGnet)

CSGnet@uiuc.edu.
bit.sci.purposive-behavior
listserv@postoffice.cso.uiuc.edu
Maintainer:
g-cziko@uiuc.edu (Gary A. Cziko)
Associated server:
WWW-server, FTP- archive
CSGnet links together those members and affiliates of the Control Systems Group who have access to electronic mail. The Control Systems Group is a collection of people from many fields, including (so far) biology, economics, education, engineering, ethology, law, management consulting, medicine, psychology (clinical developmental, experimental physiological, and social), social work, and sociology. Our common interest is exploring control theory as a way to understand behavior. Our shared conviction is that control theory offers not just an improvement of or an extension to mainstream concepts of behavior, but a replacement for them. Our aim is to continue to develop an understanding of the organization of living systems, using control-system models, to explain how behavior is generated and why it occurs.

The basic concept accepted by members of the Control Systems Group is that all organized behavior continuously controls the portion of perceptual experience which can be influenced by the actions of organisms. This is not an article of faith. It follows from a detailed quantitative analysis of behavior, showing that action affects the very perceptions on which action is based. The action might be as simple the tightening of a muscle, and the perception as elementary as the signal generated by a sensory nerve attached to a tendon Or the action might be as complex as formulating sentences, inflections, and expressions used in a conversation, and the perception as rich as judging the effects of one's communication on the attitudes of the listener, even as the words are being spoken.

As important function of the Control Systems Group is to provide a support system for people who have become dissatisfied with the quality of explanations in their own fields, and who have come to see control theory as a source of inspiration and a tool for productive and creative work.

## Newsgroup: sci.systems

sci.systems
STATUS: Unmoderated. Sci.systems provides a forum for the discussion of the theory and application of systems science. In the broadest sense, systems science is the study of the nature of systems. Such systems can be physical, chemical, biological, sociological, economic, etc. Systems science and system theory can be applied to systems of all types. Systems science as defined here includes mathematical systems analysis, systems engineering, general systems theory, etc. This definition is intentionally vague in order to encourage discussion on all aspects of the study of sytems.

Discussion might include, but is not limited to:

• An overall discussion of the various aspects of the study of systems.
• Discussion of the particular methods for analyzing systems.
• Application of different methods to particular systems.

alt.cyb-sys

## Whole Systems

wholesys-l@majordomo.netcom.com
majordomo@majordomo.netcom.com
Maintainer:
ffunch@newciv.org (Flemming Funch)
Associated server:
Whole Systems
This is the Whole Systems list for the discussion of:
• the principles of whole systems,
• abundance economies,
• the creation of a new civilization,
• whole system metaphysics
• networking and synergy
The list is for the exploration of whole system principles, particularly in regards to economic, ecological, sociological and metaphysical transformation of our civilization. The intention is to create and discuss a positive vision for the future of planet Earth as a whole system.

This is an unmoderated public list. No flaming will be allowed, but frank discussions are welcome. It is pre-supposed that the participants support the general idea of creating a better future and are able to tolerate diverse viewpoints.

Wholesys-l is a very busy list with many members and a lot of traffic. If you prefer to get only a few selected postings and no discussions, you should rather subscribe to the wholeinfo-l list. On the average one or two messages a day, which are each complete in themselves, will be forwarded from wholesys-l to wholeinfo-l. You can not post to the list directly.

## Systems and (human) Values

sysval-l@netcom.com
listserv@netcom.com
Maintainer:
martin@netcom.com (Martin L.W. Hall)
Associated server:
Systems, Values & Organizations
This is a list that tries to investigate and encourage the investigation of Systems (science), (human) values and organizations. In a nutshell, it tries to look at the human side of using systems science. Of the systems oriented lists I had not seen many that addressed the human and organizational issues of using systems science. I named it sysval because I think that the merging of systems and (human) values is of particular importance but I would welcome any issues that are related to systems science, humans and organizations.

## Autopoiesis

autopoiesis@think.net ?
listserv@think.net?
Maintainer:
palmer@think.net (Kent Palmer)
Associated server:
Thinknet

## ISSS-L

isss-l@dhvx20.csudh.edu
isss-l-Request@dhvx20.csudh.edu
Maintainer:
?
Associated server:
International Society for the Systems Sciences
List for people affiliated with the International Society for the Systems Sciences. Closed, unmoderated.

## The Observer

rwhitaker@falcon.aamrl.wpafb.af.mil
rwhitaker@falcon.aamrl.wpafb.af.mil
Maintainer:
rwhitaker@falcon.aamrl.wpafb.af.mil (Randall Whitaker)
Associated server:
The Observer Web
Discussions about autopoiesis, distinction algebras and enactive cognitive science.

## CYBSYS-L

CYBSYS-L@BINGVMB.CC.BINGHAMTON.EDU
LISTSERV@BINGVMB.CC.BINGHAMTON.EDU
Maintainer:
cybsys@bingsuns.cc.binghamton.edu (Cliff Joslyn)
This list has stopped operating because of too many other activities by the list maintainer, but you are invited to restart it if you wish to take over the administration. Contact Cliff Joslyn.

An electronic mailing list dedicated to Systems Science and Cybernetics on the SUNY-Binghamton computer system. The list is commited to discussing a general understanding of the evolution of complex, multi-level systems like organisms, minds, and societies as informational entities containing possibly circular processes. Specific subjects include Complex Systems Theory, Self-Organizing Systems Theory, Dynamic Systems Theory, Artificial Intelligence, Network Theory, Semiotics, fractal geometry, Fuzzy Set Theory, Recursive Theory, computer simulation, Information Theory, and more.

The purposes of the list include: 1) facilitating discussion among those working in or just interested in the general fields of Systems and Cybernetics; 2) providing a means of communicating to the general research community about the work that Systems Scientists and Cyberneticians do; 3) housing a repository of electronic files for general distribution concerning Systems and Cybernetics; and 4) providing a central, public directory of working Systems Scientists and Cyberneticians.

The list is coordinated by members of the Systems Science department of the Watson School at SUNY-Binghamton, and is affiliated with the International Society for the Systems Sciences (ISSS) and the American Society for Cybernetics (ASC). Different levels and kinds of knowledge and experience are represented.

# The Need for Principia Cybernetica

Author: F. Heylighen, C. Joslyn
Updated: Jun 23, 1994
Filename: PCPNEED.html

Principia Cybernetica's aim can be defined as: integrating the knowledge available in the domain of cybernetics and systems science with the help of cybernetic methods, as a first step towards integrating the whole of human knowledge available in the different disciplines. This enterprise can be motivated by the following observations:

1. knowledge at large is fragmented and in dire need of unification
2. cybernetics and systems science seems at present to be the only approach capable of bringing this kind of integration
3. cybernetics and systems science itself is in dire need of unification
4. integrating cybernetics requires an approach similar to what Principia Mathematica did for mathematics
5. however, as cybernetics is more complex, fuzzy and variable than mathematics, more powerful collaborative methods are needed to unify cybernetic knowledge
6. therefore, we need to formulate a number of more specific goals for the collaborative development of Principia Cybernetica

# Cybernetics and the Integration of Knowledge

Author: F. Heylighen,
Updated: 11 Dec, 1991
Filename: CYBINT.html

## The need for integration

It is a common observation that our present culture lacks integration: there is an enormous diversity of "systems of thought" (disciplines, theories, ideologies, religions, ...), but they are mostly incoherent, if not inconsistent, and when confronted with a situation where more than one system might apply, there is no guidance for choosing the most adequate one. Philosophy can be defined as the search for an integrating conceptual framework, that would tie together the scattered fragments of knowledge which determine our interaction with the world. Since the 19th century, philosophy has predominantly relied on science (rather than on religion) as the main source of the knowledge that is to be unified.

After the failure of logical positivism and the mechanistic view of science, only one approach has made a serious claim that it would be able to bring back integration: the General Systems Theory (von Bertalanffy; Boulding). Systems theorists have argued that however complex or diverse the world that we experience, we will always find different types of organization in it, and such organization can be described by principles which are independent from the specific domain at which we are looking. Hence, if we would uncover those general laws, we would be able to analyse and solve problems in any domain, pertaining to any type of system.

Many of the concepts used by system theorists came from the closely related approach of cybernetics: information, control, feedback, communication... In fact cybernetics and systems theory study essentially the same problem, that of organization independent of the substrate in which it is embodied. Insofar as it is meaningful to make a distinction between the two approaches, we might say that systems theory has focused more on the structure of systems and their models, whereas cybernetics has focused more on how systems function, that is to say how they control their actions, how they communicate with other systems or with their own components, ... Since structure and function of a system cannot be understood in separation, it is clear that cybernetics and systems theory should be viewed as two facets of a single approach. In order to simplify expressions, we will from now on use the term "cybernetics" to denote the global domain of "cybernetics and general systems theory". If you prefer, you may substitute "systemic" or "systems scientist" each time you will read "cybernetic" or "cybernetician".

## Cybernetic applications vs. cybernetic theory

The fundamental concepts of cybernetics have proven to be enormously powerful in a variety of disciplines: computer science, management, biology, sociology, thermodynamics... A lot of recently very fashionable approaches have their roots in ideas that were proposed by cyberneticians several decades ago: artificial intelligence, neural networks, complex systems, man-machine interfaces, self-organization theories, systems therapy ... Most of the fundamental concepts and questions of these approaches have already been formulated by cyberneticians such as Ashby, von Foerster, McCulloch, Pask, ... in the forties and the fifties. Yet cybernetics itself is no longer fashionable, and the people working in those new disciplines seem to have forgotten their cybernetic predecessors.

What is the reason that cybernetics does not seem to get the popularity it deserves? What distinguishes cyberneticians from researchers in the previously mentioned areas is that the former stubbornly stick to their objective of building general, domain-independent theories, whereas the latter focus on very specific applications: expert systems, psychotherapy, thermodynamics, pattern recognition, ... These applications attract attention insofar that they are useful, concrete or spectacular. On the other hand, the aim of general integration remains too abstract, and is not sufficiently successful to be really appreciated.

But why then is cybernetics less successful than these more trendy approaches? Clearly the problem of building a global theory is much more complex than any of the more down-to-earth goals of the fashionable approaches. But we may also say that the generality of the approach is dangerous in itself if it leads to remaining stuck in abstractions, which are so far removed from the everyday world that it is difficult to use them, interact with them, test them on concrete problems, in other words, get a feel on how they behave and what are their strengths and weaknesses.

## Unifying theory and applications

Our contention here is that the goal of global integration is still, if not more, of an essential importance, but that cybernetics has a number of lessons to learn from its more specialised applications. Whereas cybernetics aims to unify science, it is in itself not unified. Instead of looking down on practical applications, it should try to understand how those applications can help cyberneticians in their task of unifying science, and first of all unifying cybernetics. It should look upon them as tools, that can be used for tasks that may extend much further than the ones they were originally designed for.

Where the theory of cybernetics can be enriched by its applications, we may similarly expect that the applications will be enriched by a closer contact with the general theory from which they originated. There is now already a trend in many of those fashionable approaches such as expert systems design, robotics, man-machine communication, etc., to acknowledge the limitations of their specific paradigm, and to look back to a broader, "cybernetical" framework for inspiration on how to overcome them. In conclusion, what we are arguing for is a cross-fertilization between cybernetics and its various recently fashionable applications.

The reason we believe that the time is ripe for such an approach is that both cybernetics and its applications have reached a sufficient level of maturity that it seems realistic to integrate them in practice. The present situation in cybernetics may be compared with the situation in Mathematics at the end of the previous century. What cybernetics needs is support for coping with the practical complexity of its problem domain, and a concrete filling in of some of the main "slots" in its empty framework. What the applications need is a framework in which they can be fitted, brought into contact, and situated the one with respect to the other. This should bring cybernetics back into contact with reality, and help it to succeed in its overall goal of integrating the different systems of thought.

# Principia Cybernetica & Principia Mathematica

Author: F. Heylighen,
Updated: Dec 1991
Filename: PRMAT.html

## Principia Mathematica

Around the end of the last century, mathematics proposed a great variety of very successful applications: geometry, calculus, algebra, number theory, etc. Yet there was no overall theory of mathematics: these different domains functioned mainly in parallel, each with its own axioms, rules, notations, concepts, ... Most mathematicians would agree intuitively that these different subdisciplines had a "mathematical way of thinking" in common, but one had to wait for the development of mathematical logic by Boole and Frege, and set theory by Cantor before this way of thinking could be formulated more explicitly. Yet set theory and formal logic were still plagued by incoherence, paradoxes, inconsistencies and lacking connections.

One had to wait further for the classical work of Russell and Whitehead, the Principia Mathematica, in which they ground the "principles of mathematical thinking" in a clear, apparently consistent and complete way. (the theorem of Gö;del later shattered the hope that such a formal treatment could ever be considered complete, but that is another story). What was novel in the work of Russell and Whitehead was that they applied mathematical methods to the foundation of mathematics itself, formulating the laws of thought governing mathematical reasoning by means of mathematical axioms, theorems and proofs. This proved highly successful, and the Principia Mathematica stills forms the basis of the "modern" mathematics as it is taught in schools and universities.

## From mathematics to cybernetics

Our contention is that something similar should be done with cybernetics: integrating and founding cybernetics with the help of cybernetical methods and tools. Similar to the mathematical application domains (number theory, geometry, etc.), the applications of cybernetics (neural networks, systems analysis, operations research, ...) need a general framework to integrate them. Similar to the integrating theories of mathematics at the end of the 19th century (Cantor's set theory, formal logic, ...), the integrating theories of cybernetics at the end of the 20th century (general systems theory, second-order cybernetics, ...) are not integrated themselves. In reference to Russell and Whitehead, the present plan for integrating them may be called the "Principia Cybernetica Project" (Turchin, 1990; Joslyn, 1990; Turchin, Joslyn and Heylighen, 1990).

## Comparing mathematics and cybernetics

Let us further indicate the similarities and differences between a Principia Mathematica and a Principia Cybernetica. Both mathematics and cybernetics are in the first place metadisciplines: they do not describe concrete objects or specific parts of the world; they describe abstract structures and processes that can be used to understand and model the world. In other words they consist of models about how to build and use models: metamodels (Van Gigh, 1986). This meta-theoretical point of view is emphasized in particular in the so-called "second order cybernetics" (von Foerster, 1979; 1981), which studies how observers construct models.

It is because of this metatheoretic character that mathematics and cybernetics can be applied to themselves: a metamodel is still a model, and hence it can be modelled by other metamodels, including itself (Heylighen, 1988). In mathematics, the most well-known example of such a self-representation is the technique of "Gödelization", where a proposition about natural numbers is represented by a natural number. Of course, it is well-known that any self-representation must be incomplete (LÖfgren, 1990), as illustrated by the Gödel theorem, but we do not consider completeness in the formal sense to be a necessary condition for a practically functioning modelling framework.

Let us proceed with the differences between cybernetics and mathematics. Mathematics is characterized by the following assumptions: simplicity, regularity and invariance; the separability of systems into independent elements; and the objective, context-independent, value-free character of knowledge. Cybernetics, on the other hand, emphasizes complexity, variety and process; the fact that elements always interact; and the subjective, context- and value-dependent nature of knowledge. Cybernetics does not deny the value of mathematics; it assumes it but goes beyond it, by trying to encompass phenomena which cannot be represented in a static, unambiguous, formal framework. It is clear then that the self-application of cybernetics, in the form of a Principia Cybernetica, must be different from the Principia Mathematica model. A Principia Cybernetica must put the emphasis on evolution and open-endedness, on different levels of precision or vagueness, on dynamic interactions between a variety of systems or viewpoints.

Part of the reason why the General Systems movement in the fifties and sixties did not succeed in its objectives was because its models and methods were still too dependent on the static, atomistic paradigm that gave birth to mathematics and classical mechanics. The reason why the present situation is much more promising is that now we dispose of better concepts, tools (e.g. computers), and methods for modelling complex and dynamic phenomena (Heylighen, 1989).

## Principles of cybernetics

Yet the idea of developing general principles (Principia) still assumes a striving towards clarity, "objectivity", and invariance. We do not want to get trapped in endless discussions and confusions over subjective meanings and viewpoints. The invariant principles that are to be derived, however, will be situated at such a high level of abstraction that they do not impose any absolute restrictions on concrete questions. They will form an empty skeleton or framework on which a multiplicity of more concrete theories can be hung (cf. Boulding, 1956). This framework will function primarily as a heuristic tool for building models, which will not preclude any model, but which will provide guidelines for avoiding difficulties and for making models more adequate. In order to succeed in that, the framework will need to incorporate methods for concretizing its own recommendations, depending on the context of the problem. This means that, unlike mathematics, the framework should provide many intermediate levels between the abstract, precise and invariant principles and their concrete, context-dependent implementations.

# Principia Cybernetica as a Universal Knowledge System for Cybernetics and Systems Science

Author: C. Joslyn,
Updated: Jan 1992
Filename: PCPUNKNO.html

[Node to be completed]

The potential for computer technology to revolutionize knowledge systems is of course not only well known, but also well underway. There are many \cite{BUV45,END63,NET65} who have championed the above ideas into the idea of constructing a "universal knowledge system" which would not only dynamically represent the current state of knowledge, but also make it accessible at multiple levels of resolution and in multiple orderings. Such a system is envisioned to have: universal access to all individuals and groups; universal content of all representable knowledge; unlimited "collaborative granularity" to group people and groups of people into other groups of people; a completely connected architecture, to ensure accessibility of the whole system; complete flexibility of representational form and modality; and a maximal interface through human sense organs and effectors, perhaps to the point of neural interfacing.

Of course, such a goal is still quite remote, for those researchers or any others in any field. Yet where else but in Cybernetics and Systems Science should this be seriously attempted? Where else are the fundamental principles of information systems so well understood and developed? Where else is this intimate relation between people and machines more highly championed? And, what other field could most benefit from the possibility of an easing of the construction of a unified conceptual territory from a vast, heterogeneous expanse?

# Specific Goals for Principia Cybernetica

Author: C. Joslyn,
Updated: Jan 1992
Filename: ^PCPGOALS.html

Principia Cybernetica has the following specific goals:

Collaboration:
For a group of researchers, perhaps not all geographically close, to collaboratively develop a system of philosophy. The task of growing such a system should be beyond the grasp of any one individual. In order to achieve progress, openness, and the participation of the scholarly community, balance in the content of the system must be reflected by a balance of opinions of its authors and between editorial control and public participation.

Constructivity:
To produce a system of philosophy that can develop dynamically over time, with continuing refinement and expansion, while retaining a record of its history. Such a system must be "grown"--it will begin small, and become larger. But change in the philosophy must not only be in its growth, but also in revision, the correction of error, and incorporation of new opinions and participants. Thus it must be possible for parts of the system to be changed and deleted on an ongoing basis.

Active:
The content of the project should not just be a passive reflection what the authors construct, but be a an model able to generate its own activity, and to act on itself and its organization. The structure of the system should not just represent the principles being developed, but also manifest them in its actions.

Semantic Representations and Analysis:
For the system of philosophy to fully reflect and incorporate the multiple semantic relations inherent among the terms being explicated, and to unify and synthesize notations and the senses of terms as used in different disciplines. The semantic relations among the terms and concepts are complex and intricate. In this way, knowledge can be represented in its breadth, depth, and other orderings as conceived by the readers and authors. The coherence of a system of thought is aided by the unification and synthesis of terminology. Much of the development of the system will be done through the explication of concepts and the multiple senses of terms in the context of their history in the literature.

Consensus:
To support the process of argument and dialogue among experts toward the development of consensually held views among a number of researchers, while preserving their individual views.

Multiple Representational Forms:
To support mathematical notation and the easy movement among natural language, formal language, and mathematics, and to support bibliographical and historical reference. There are many different forms of linguistic expression aside from natural language which are very useful for philosophical work. These include graph notation (nodes and arcs), set notation, predicate logic, mathematical notation, and other forms of lists, tables, and diagrams.

Flexibility:
To allow researchers to develop or read the philosophical system in various orders and in various degrees of depth or specificity. It must be possible for readers to have access to all of the orderings and dimensions of this large multi-dimensional semantic system, and to travel freely along and among them.

Publication:
To support the traditional publication of different stages of parts or the whole of the philosophical system and of various special purpose documents, including journal articles, books, dictionaries, encyclopedias, texts on a subject, reference pages, essays, dialogues on a subject, or "streams of consciousness".

Multi-Dimensionality:
To allow the representation and utilization of knowledge in its breadth, depth, and any other arbitrary orderings.

# What is the meaning of life?

Author: F. Heylighen,
Updated: Dec 2, 1997
Filename: MEANLIFE.html

 Synopsys: The meaning of life is to increase fitness

This is the quick answer to this fundamental question. In order to start giving the long answer, we should first examine each of the key terms in this sentence:

meaning:
a very complex concept which can have many interpretations. In this context we will assume it signifies the "why" (origin - past) or "wherefore" (purpose - future) of life, but in a way our answer also may explain us the "what" (definition - present).
life:
in this context it normally means our present being here on earth, but this may be generalized to include life as a particular type of organization and development characterizing biological organisms, and even more universally as organization and development in general.
fitness:
intuitively, a system, configuration or "state-of-affairs" is fit if it is likely that that configuration will still be around in the future. The more likely we are to encounter that system, the more fit it is. Though there are many ways to be fit, depending on the exact situation, we may say that fit systems tend to be intrinsically stable, adapted and adapting to their surroundings, capable of further growth and development, and/or capable of being (re)produced in great quantities.

Fitness is the most important and tricky term of the answer to define. It can only be defined in terms which are not obvious themselves, and so need further definitions, and so on. One can hope that after a few rounds of definitions, the meaning will become sufficiently intuitive to be satisfactory for most readers. The whole of Principia Cybernetica Web can be viewed as an attempt to provide a sufficiently extensive semantic networks of concepts clarifying concepts (such as "fitness").

increase:
this should be obvious enough. The use of the term "increase" implies that the concept to which it is attributed, "fitness", is to some degree quantifiable (see e.g. a definition in terms of transition probabilities). Note, however, that it is everything but obvious how to do this: fitness is difficult to measure, and is relative, depending on situation, environment and moment in time. At the very least, we assume that there exists a partial ordering, i.e. some configurations are more fit than others. A more general form of the answer is "not to decrease fitness": in some circumstances it may be good enough to keep fitness the way it is. Increase of fitness determines a gpreferred direction of evolution.
We may conclude by paraphrasing the answer in the following way: the purpose of (living) organization is to continuously increase future probabilities of encountering this same type of organization. The argumentation for this can be found in the variation and selection principles of evolution.

## "Higher" values

The above definition has been criticized as being overly reductionist, trying to reduce higher, "spiritual" meanings to mere biology. Although the concept of fitness originated in biology, its meaning here is much wider. It can be argued that our higher mental faculties and values are direct extensions of the general concept of fitness.

"Self-actualization", Maslow's term for maximally developing all our protentialities, and thus reaching the highest level of psychological health and awareness, is merely the implementation of fitness increase in the mental domain (see my paper on Maslow). Similarly, it can be argued that happiness is a direct sign that we have managed to improve our fitness. Thus, if people say that the meaning of life is to "learn and develop", "actualize our potentialities", "improve the balance of pleasure and pain", "enjoy ourselves" or "simply be happy", they are expressing a more limited version of the answer above (limited in the sense that it is more difficult to apply to non-human life, and does not take into account other aspects of life).

On the other hand, people who express the belief that the meaning of life is to "love and be loved", or "promote cooperation and togetherness" are expressing the importance of our social needs, which are another component of fitness. Indeed, fitness for individuals requires fitness for the group to which these individuals belong, and this implies cooperation and "love" rather than selfishness and hostility.

Even those people who state that "life has no meaning" do not contradict the present definition. Indeed, if "meaning" is seen in the restricted sense of a fixed, external purpose, then life has no meaning. "Increasing fitness" is not a goal explicitly imposed by some God, but rather the "implicit goal" governing all of evolution. There are an infinite number of ways in which fitness can be increased, so we cannot say that life necessarily has to move to one end state rather than another. Most changes are initially blind. It is just that some directions (those that decrease fitness) are likely to be eliminated sooner or later by selection.

We remain free in choosing which of the directions we will take: goals or values are not imposed on us. The fitness criterion is merely a guideline to help us choose those most likely to prolong and develop life. But the final decision will depend on our our personal circumstances, and therefore requires reflection. In that sense, the present answer also encompasses the answers of those people who state that the meaning of life is "a personal choice", "to be found within oneself", or even "to ask the question 'What is the meaning of life?'".

## Other philosophical questions

The above answer provides a foundation for answering other fundamental questions of philosophy, including:
What exists? (ontology)
configurations that have a minimal fitness. Below a certain fitness treshold, phenomena are so variable, or fleeting that they cannot be observed in any objective manner, and have no causal influence on anything else, so we might as well say that they don't exist. Examples are "virtual particles" in quantum field theories.
What is (true) knowledge? (epistemology)
fit models or representations of fit configurations. Phenomena with low fitness are too unstable to allow reliable models (see previous paragraph). Good models should satisfy some additional criteria in order to be fit themselves.
How should we act? (ethics)
by doing things that increase our own long-term fitness, taking into account the fitness of the systems (society, ecosystems) to which we belong. Enhancing long-term fitness is the fundamental good, or basic value of our philosophical system.
For more answers to the "meaning of life", see:

# History of the Principia Cybernetica Project

Author: Heylighen, Joslyn, Turchin
Updated: Jun 19, 1998
Filename: HISTORY.html

## Origin of the Project

The Principia Cybernetica project was conceived by Valentin Turchin, a physicist, computer scientist, and cybernetician, whose political activity and antitotalitarian views led to his forced emigration from the Soviet Union to the United States in 1977. He had developed a cybernetic philosophy based on the concept of the "metasystem transition" with implications for human evolution, political systems, and the foundations of mathematics. He further wanted to develop an integrated philosophical system with a hierarchical organization, and involving multiple authors.

In 1987, Turchin came into contact with Cliff Joslyn, a systems theorist, software engineer, and proponent of Turchin's philosophy. After discussing Turchin's ideas for a collaboratively developed philosophical system, Joslyn suggested a semantic network structure using hypertext, electronic mail, and electronic publishing technologies as a viable strategy for implementation, maintenance, and production of such an ambitious project. Together they founded the Principia Cybernetica Project and formed its first Editorial Board. They wrote a first general proposal, and a document they called "The Cybernetic Manifesto" in which the fundamental philosophical positions were outlined. Joslyn began publicizing Principia Cybernetica by posting the relevant documents on the CYBSYS-L electronic mailing list in the autumn of 1989.

This generated a fair amount of response, including that of Francis Heylighen, a physicist, cognitive scientist, and systems theorist. He reacted with detailed comments on the content of the Project (the evolutionary philosophy), its form (the hypermedia organization of knowledge), and the link between the two. Heylighen had been developing a very similar philosophy to Turchin's and had been thinking along the same lines of creating a network of people interested in the domain of complex, evolving systems who would communicate with the help of various electronic media. He started an active correspondence with Turchin and Joslyn, and finally joined them as the third member of the editorial board in spring 1990.

## First Public Activities

Other reactions to Principia Cybernetica were more contentious. The strong tone of the "Manifesto", which was intended to provoke reaction, engendered a sometimes heated debate on the CYBSYS-L list, where several fundamental criticisms were made, leading the PCP-editors to carefully evaluate the wording of the project. The Manifesto became the first of many publications devoted to PCP, written by the editors and other contributors.

The first official activity of PCP was the sponsorship of a forum on Cybernetics and Human Values at the 8th Congress of the World Organization of Systems and Cybernetics at Hunter College in New York in July of 1990. The Editorial Board were joined by B. Lichtenstein and D. White in a forum which introduced PCP and discussed many of the relevant issues.

Following this forum the editors not only forged coherent working relationships, but were able to come to considerable consensus not only about issues of philosophical content, but also of management and organization.

The publication of the Principia Cybernetica Newsletter # 0 followed, which was widely distributed to members of the cybernetics and systems community by postal mail and computer networks. The Newsletter garnered many favorable and some critical responses from our colleagues, and the Editors proceeded to organize the 1st Principia Cybernetica Workshop, held at the Free University of Brussels during 5 days in July, 1991. This gathering was very successful and well attended, resulting in the publication of the Workbook containing extended abstracts of the papers presented at that meeting; and the Newsletter # 1.

1991 also saw the establishment of the PRNCYB-L electronic mailing list. PRNCYB-L is now used as a discussion medium for over 100 project participants.

## Grants, Awards and Conferences

PCP is very pleased to have received several grants. Three of them were awarded by the Belgian "National Fund for Scientific Research": one in 1992 for a collective project on Knowledge Development, one in 1993 for the individual project of F. Heylighen devoted to the network support for PCP, and another one in 1994, extending the previous project on Knowledge Development. The latter grant included a contract for a full-time research assistant to support the project. That position was given to Johan Bollen, who started to work for PCP in January 1994. F. Heylighen also received a grant specifically for his PCP-research in 1993 from the "Cultural Support Fund" of the Free University of Brussels. A paper by Heylighen on PCP, entitled "Principles of Systems and Cybernetics", received a "Best Paper Award" for the Symposium on "General Systems Methodology" at the 11th European Meeting on Cybernetics and Systems Research in Vienna, 1992.

Following the success of the 1991 Workshop, PCP organized several other conferences, starting with a one-day symposium at the 13th International Congress on Cybernetics in Namur, Belgium in August 1992. A symposium on "Cybernetic Principles of Knowledge Development" was held at the 12th European Meeting on Cybernetics and Systems Research in Vienna, in April 1994 (at the same congress, the Principia Cybernetica Web was publically demonstrated). A very well-attended 3 day Symposium on "The Evolution of Complexity" was organized in Brussels at the "Einstein meets Magritte" conference, in June 1995, and a symposium on "Theories and Metaphors of Cyberspace" was organized in Vienna in April 1996.

## (Electronic) Publications

1993 is the year in which PCP first realized its aim of world-wide electronic publication of its material (sound). First, in March, an anonymous FTP-server was established at the Free University of Brussels, followed in July by a World-Wide Web distributed hypertext server (which turned out to be the first one in Belgium). Since then, the use of the Web server has been steadily growing, from a few dozen to some 8000 requests a day. In 1994, the Web was enhanced with several new tools: a searchable index, color photographs, a clickable map, a permanent menu bar, and the possibility to make annotations. This resulted in very positive reviews, including a "Honorable Mention" in the Best of the Web competition. September 1994 saw the first experimental application of PCP principles: an adaptive semantic hypertext. The "recent changes" provide a detailed chronology of the different additions and changes in the Web.

1994 was the year in which PCP had been active for five years, leading us to produce a Progress Report. It concluded that, in spite of the great initial ambitions and rather limited means of the project, quite a lot had been achieved. In 1995, a special issue of "World Futures" was published on "The Quantum of Evolution". This collection of invited papers, edited by the PCP board, provided the first extensive overview of the theoretical framework developed by PCP.

In the autumn of 1995, a second electronic mailing list, PCP-news, was installed for the distribution of announcements. The digest of messages sent to that list provides a detailed account of the developments since then, such as the different "spin-off" groups that PCP helped start up, which include the Global Brain Group, the Journal of Memetics and the study group on Progress.

# PCP-news digest

Author: F. Heylighen
Updated: Sep 8, 1998
Filename: PCPNDIGE.html

The following is a digest of the main news sent every two month to the PCP-news mailing list, chronologically ordered.

## News - May/June 1996

The last two months have been relatively quiet on the front of new nodes or email discussions. On the other hand, there has been an important meeting of the Principia Cybernetica editorial board in Washington DC, accompanied by some seminars, discussions and a "cybernetics party" (see http://gwis2.circ.gwu.edu/~joslyn/96summer.html for a program of the events).

The most important result of the meeting was a new consensual definition of the central concept of "control" together with a number of related concepts. A draft node has already been put on the Web (see below), but it will be elaborated with many more details and related nodes in the coming two months.

It may be of interest to note that a new mailing list, j-memetics, has been created, which is to some degree a "spin-off" of PRNCYB-L. Its aim is to discuss the creation of a peer-reviewed, electronic journal devoted to memetics or "evolutionary models of information transmission". For more info, contact hanss@sepa.tudelft.nl (Hans-Cees Speel) or b.edmonds@mmu.ac.uk (Bruce Edmonds).

## News- July/August 1996

Last August, a new study group, associated with PCP, has been started, to discuss the emergence of a "global brain" out of the computer network, which would function as a nervous system for the human "superorganism". Participation is limited to people who have been doing active research and published books or papers on this subject. Present members are: Peter Russell, Gottfried Mayer-Kress, Gregory Stock, Lee Chen, Johan Bollen, Ben Goertzel, Joel de Rosnay, Valentin Turchin and Francis Heylighen. For more info, see http://cleamc11.vub.ac.be/suporgli.html or contact Francis Heylighen.

## News- Sept/Oct 1996

A first part of the new results, reached during the PCP board meeting in June, on the definition of control have now been incorporated into PCP Web (http://cleamc11.vub.ac.be/control.html). Moreover, our programs for self-organizing hypertext and retrieval of words through spreading activation can now be permanently consulted on the web, via a new node devoted to our research on learning, "brain-like" webs (http://cleamc11.vub.ac.be/learnweb.html).

The PCP editor Cliff Joslyn has moved from Goddard Space Center, NASA, to the Los Alamos National Laboratory. His new address is:

• Mail Stop B265

• Los Alamos National Laboratory

• Los Alamos, NM 87545 USA

• joslyn@lanl.gov

• http://gwis2.circ.gwu.edu/~joslyn

The groups associated with PCP have also been quite active. The people involved with the electronic "Journal of Memetics" have reached consensus on an introductory text describing the aims of the journal, a general editorial policy, a managing editor (Hans-Cees Speel, hanss@sepa.tudelft.nl), and the constitution of an advisory board (presently Daniel Dennett, Aaron Lynch, David Hull and Gary Cziko). At the moment, they are looking for authors wishing to contribute to the first issue, which is scheduled for 1997. If you are interested to write a paper or take part in the reviewing process, please contact Hans-Cees Speel.

The "Global Brain" group (see http://cleamc11.vub.ac.be/gbrain-l.html) has started its discussions on superorganisms and networks playing the role of nervous systems. Thanks again to Bruce Edmonds (who already created the PRNCYB-L archive, and the Journal of Memetics list and web site), an archive of the discussions can now be consulted at http://www.fmb.mmu.ac.uk:80/~majordom/gbrain/

## News- Nov/Dec 1996

We are happy to announce that Joel de Rosnay is joining PCP as a new "associate" (see http://cleamc1.vub.ac.be/masthead.html). Joel is a systems theorist, futurologist, molecular biologist and prolific writer of popular science books on topics related to the cybernetic world view. He is presently Director of Strategy of the Cite des Sciences et de l'Industrie at La Villette (Paris, France). His book "The Macroscope", a systemic view of the world as whole, will soon be made available on the Web with the support of PCP. Joel is in particular interested in the "cybernetic organism" formed by global society and its "planetary brain" emerging out of the computer networks. His home page, with interviews and excerpts from his work can be found at http://www.cite-sciences.fr/derosnay/e-index.html

The people involved with the electronic "Journal of Memetics", associated with PCP, have set up their (still experimental) web site for the publication of memetics related articles. The first papers should be published in the next few months.

## News- Jan/Feb 1997

The most important development was the publication on PCP Web of a complete book on the system's approach, "The Macroscope", by PCP associate Joel de Rosnay.

## News- March-April 1997

After the very successful Web publication of Joel de Rosnay's book "The Macroscope" (which has drawn many positive responses), we are preparing a Web version of another difficult-to-find classic book on cybernetics and systems thinking: "The Phenomenon of Science. A cybernetic approach to human evolution" by PCP editor Valentin Turchin. The book should be available on PCP Web in the coming weeks.

We are preparing the annual meeting of the Principia Cybernetica Editorial Board (F. Heylighen, C. Joslyn, V. Turchin and J. Bollen) in Brussels. It is likely to take place during the last week of June, and to include a visit to Paris for discussions with PCP associate Joel de Rosnay. It might also be accompanied by a seminar on Metasystem Transition Theory at the Free University of Brussels.

The "Journal of Memetics - Evolutionary Models of Information Transmission", associated with PCP, is ready to go on-line with its first issue. After peer review, four papers and a book review have been accepted for publication. Once the website has been thoroughly tested, its URL will be announced through this and other mailing lists. Richard Dawkins has agreed to join the Journal's advisory board.

## News- July/August 1997

As could be expected, there was not much activity during the summer months. As announced earlier, the PCP office has moved to a different building within the Free University of Brussels, and is now housed together with the associated Center Leo Apostel. New phone, fax, mail etc. addresses are listed on PCP's masthead (http://cleamc11.vub.ac.be/MASTHEAD.html). The move of the project's web server to the new physical location, which is connected to the network by a microwave antenna, went surprisingly smoothly. An increase in the number of system crashes after the move seems now to have been solved by updating the network software.

ASSOCIATED GROUPS

The opening up of the mailing list of the Global Brain Group to selected non-members has produced a lot of additional discussions. About a dozen new subscribers with diverse backgrounds have joined the list. The archive of messages can be consulted at http://www.cpm.mmu.ac.uk/~majordom/gbrain/

During a stay in Jan's summer house in the (French) Provence, Jan Bernheim and Francis Heylighen have further developed their ideas for a study group that would focus on an evolutionary analysis of social progress. It starts from the observation that practically all indicators for average quality of life (wealth, life expectancy, level of education, equality, democracy, literacy, IQ, life satisfaction, ...) have undergone a spectacular increase during the last half century. ( see http://cleamc11.vub.ac.be/CLEA/Groups/Progress.html). This undeniable progress for humanity as a whole stands in sharp contrast with the prevailing pessimism of many commentators or the relativism of the postmodernists. The main aim of the group is to analyse these trends critically, and to explain them on the basis of evolutionary principles. This may lead to practical and ethical guidelines for future developments.

This group would be associated with PCP, in a way similar to the "Global Brain Group". This means that the group works on a more specific subject within the larger evolutionary-systemic world view which PCP is developing, thus providing a more specialised focus, while including both PCP members and others. People interested in participating in this study group may contact Francis Heylighen .

## News- May/June 1997

BOARD MEETING

In the period June 20-30, the annual meeting of the Principia Cybernetica Editorial Board (F. Heylighen, C. Joslyn, V. Turchin and J. Bollen) took place in Brussels. It included a visit to Paris for discussions with PCP associate Joel de Rosnay, and a meeting at the Center Leo Apostel of the Free University of Brussels. The discussions centered on a whole range of issues concerning the organization of the project and its philosophical content. The general state of the project, its web server and associated groups were reviewed.

Some of the practical issues discussed were the editing of nodes by editors at a distance (e.g. using Netscape Gold), and the conversion of LaTeX formatted texts (including a number of PCP papers by Turchin and Joslyn) to HTML. It was decided to try to program a simple converter in Perl, rather than install one of the cumbersome conversion packages that already exist. It was also decided to develop an animated version of the PCP logo, which would illustrate the process of metasystem transition as an infinite recurrence. More advanced interface issues for the organization of Principia Cybernetica Web were discussed, such as the use of frames or Java applets, but no concrete decisions were as yet taken.

At the content level, we focused on the central node about Metasystem Transition Theory, rewriting its text and reorganizing its child nodes. In particular, we decided to add a new "methodology" node. We further discussed different topics, including ethics, the global brain and the idea of progress. Cliff Joslyn proposed a new representation for the central concept of "control", thus extending the one developed at last year's board meeting in Washington DC.

We further discussed a number of recent developments in intelligent computer networking, such as the use of ontologies, semantic webs, link types in hypertext, groupware, multidimensional clustering to develop higher level concepts, graphical representations of complex web structures, agents, and spreading activation. All these technologies are potentially useful to make PCP web more intelligent and user-friendly. Moreover, they are likely to be included in one of the different research proposals being prepared by PCP members and others at the Los Alamos National Research Laboratory, the "Global Brain" study group, and the Free University of Brussels. It was concluded that we need to get a good grasp of the present "state of the art" for these technologies. This will help us to clarify, integrate and strenghten the different proposals.

The meeting with a number of researchers of the Center Leo Apostel (CLEA, see http://cleamc11.vub.ac.be/CLEA/) replaced a planned seminar on Metasystem Transition Theory (MSTT), which was cancelled for practical reasons. The discussion confronted PCP's MSTT with CLEA's research project on transitions between layers of reality. The parallels between both approaches were clear, and it was decided to try and integrate the "control levels" of MSTT and CLEA's "reality layers". This would entail an extension of the known sequence of metasystem transitions down to the level of quantum mechanics, according to the formula: classical mechanics = control (constrained variation) of quantum non-locality. Thus, the hypothesized MST would reduce the infinite dimensional Hilbert space of quantum phenomena to the three dimensional Euclidean space of classical mechanics.

The meeting with Joel de Rosnay at the "Cite des Sciences" in Paris focused mainly on the development of the "Global Brain" theme. de Rosnay suggested to organize a conference on the subject, and to arrange funding for research through various institutions with which he has good contacts. He told us that Microsoft chairman Bill Gates, with whom he is acquainted, expressed particular interest in PCP. We agreed that if de Rosnay does not find a publisher for the English translation of his 1995 book "L'homme symbiotique" (Symbiotic Man), we would publish it on PCP web, like we did with his 1975 book "The Macroscope". de Rosnay said he would send us a representation of his own "spiral" model of transitions to higher level of complexity, for inclusion in PCP web.

ASSOCIATED GROUPS

In the last week of May, the "Journal of Memetics - Evolutionary Models of Information Transmission", associated with PCP, went on-line with its first issue (http://www.cpm.mmu.ac.uk/jom-emit/). The website is getting more and more popular, and the associated mailing list for memetics discussions has become very active, with 500 messages in its first 5 weeks (see the archive at http://www.cpm.mmu.ac.uk/~majordom/memetics/). However, there are not as yet many new proposals for papers, and authors are still solicited to submit manuscripts.

The "Global Brain" study group has decided to open up its mailing list to selected subscribers ( see http://cleamc11.vub.ac.be/gbrain-l.html ). The reason is that the founding members were too busy and their number was too small to sustain active discussions. However, since the global brain topic is bound to attract many mudheads and crackpots, while we wish to keep the intellectual level and signal-to-noise ratio of the discussion high, we agreed about a selection procedure on the basis of the submissions of prospective new members.

## News- Sept/Oct 1997

Alex Riegler, an Austrian cognitive scientist, has applied for a postdoctoral visiting fellowship at the Brussels office of PCP. If the application is accepted by the funding agency, he will start to work here on Feb. 1 for a period of one year (and possibly longer). Alex has been doing research on cognitive constructivism and the systems theory of evolution, applied to the design of autonomous agents. For further details and publications about this quite interesting work, see his home page: http://www.ifi.unizh.ch/~riegler/

Valentin Turchin's book "The Phenomenon of Science" has finally been completely scanned in. (the work was delayed because An Vranckx, who was working on the scanning, has been abroad for several months). Once the text has been converted to HTML and integrated with all the figures, the book will be made available on PCP web. We hope to make the official announcement within the next few days.

The study group on "Progress" associated with PCP has had its first informal meeting in Brussels, just before a seminar on "Understanding Happiness" by Ruut Veenhoven, a Dutch sociologist who has done extensive research on the social, economic and psychological factors involved in life-satisfaction. (see his World Database of Happiness: http://www.eur.nl/fsw/soc/happiness.html) Veenhoven himself was enthusiastic to join the group and to collaborate on a joint research proposal. It was agreed to start preparing an edited book, in which different contributors would discuss the different aspects and mechanisms of global progress, such as economic growth, increase in life expectancy, raise in education level and IQ, and improvement in the overall the quality of life. The book is planned to be ready by the year the 2000. Veenhoven suggested the title "The Optimistic Manifesto", but this is of course still open for discussion.

## News- Nov/Dec 1997

VARIOUS ACTIVITIES

In spite of the intervening holidays, November and December were quite busy months for the PCP team. PCP editor Val Turchin's book "The Phenomenon of Science" was finally published on the web, and attracted quite some interest.

Two meetings were announced, a "Symposium on Memetics" (http://cleamc11.vub.ac.be/MEMETSY.html) chaired by PCP editor F. Heylighen and PRNCYB member Mario Vaneechoutte, and a Special Session on "Semiotics of Autonomous Information Systems" (http://www.c3.lanl.gov/~joslyn/ISAS98/) chaired by PCP editor Cliff Joslyn and PCP associate Luis Rocha. Though the meetings concern different topics, they fall in about the same period, respectively August and September 1998. The first is organized by the "Journal of Memetics" associated with PCP and is part of the 15th Int. Congress on Cybernetics, the second is part of the 1998 Conference on Intelligent Systems and Semiotics.

Although not officially associated with PCP, it is worth mentioning the creation of the new "Journal of Complex Systems" (http://www.santafe.edu/~bonabeau/), edited by our friend Eric Bonabeau from the Santa Fe Institute. The general subject is close to PCP themes, and PCP editor Cliff Joslyn is member of its editorial board.

It has now been confirmed that Alex Riegler, an Austrian cognitive scientist, will come to work at the Brussels PCP office in February. Although his application to the Belgian Fund for Scientific Research was not accepted, he managed to get money for a year's stay from the Austrian National Bank.

The study group on "Progress", associated with PCP has submitted a research project entitled "Progress in global quality of life and its indicators: an interdisciplinary approach" for funding. The aim is to analyse a host of statistical data in order to study in how far the on-going development and modernization of society is associated with increase in happiness, and thus to test the evolutionary theory underlying PCP in the domain most relevant to our present situation. If the project is accepted, this will add another researcher to the PCP team in Brussels, and provide us with some more money. The promoters of the project are Francis Heylighen, Jan Bernheim, Ruut Veenhoven and Robert Scott Gassler.

THE PRINCIPIA CYBERNETICA WEB

The last part of 1997 was quite unlucky for PCP's technical infrastructure. First, the PRNCYB-L mailing list in Binghamton, NY, broke down for several weeks. Then, the PRNCYB-L archive in Manchester, UK, suffered a disk crash, so that several messages got lost. Finally, on Dec. 5, the main PCP web server in Brussels, Belgium, had a hard disk crash, caused by an electricity cut-off. Because of poor backing-up procedures (which will be remedied soon), the most recent copy of the material we had was 6 months old, so that lots of files were missing.

Happily, a call for help to this mailing list produced a deluge of reactions from people who had kept copies of PCP files. Two of them even had used a web robot to gather a complete copy of PCP web, which was not more than two weeks old. This allowed us to restore all lost files, though the robot produced a number of small formatting changes, which had to be undone. Because of that, you may still find a few errors in URL's in different PCP nodes. Please let us know if you find one, so that we can correct everything.

Thanks again to all those who offered their help. Because of you, PCP web could be restored with a minimum of delay. Your massive response showed how PCP has gathered a wide audience of people actively interested in our project. This group continues to grow, as shown by the 3 to 4 new subscribers this mailing list gains every week (while very few people ever unsubscribe), and by the many email reactions we receive.

It seems that the number of people actively interested grows more quickly than the number of hits on our server (at present inching towards 8000/day). This is probably caused by the massive increase in servers and web pages on the net, which compete for the attention of a more slowly increasing number of web surfers. The result is that the new users PCP web attracts will be lower in number, but higher in their interest for the specific PCP themes. When PCP web was created, there were only some 200 servers in existence, and practically every server was interesting for those exploring the new medium. Nowadays there is such an overkill in available information, that only those really motivated to study cybernetic philosophy are likely to discover, and do the effort to explore, PCP web.

## News- Jan/Feb 1998

After the busy activities at the end of 1997, the beginning of 1998 was relatively quiet. Alex Riegler has started to work at the Brussels PCP office at the end of January. He has joined the project as an editorial assistant. The new address of his home page is http://cleamc11.vub.ac.be/riegler/ He has also submitted a research project on "Evolutionary Complexification of Knowledge Systems". If this application is accepted, he will get a 3 year postdoc research contract to work with us.

Contributions for two PCP co-organized sessions are being collected. (see http://cleamc11.vub.ac.be/act.html) If you consider submitting an abstract to the symposium on memetics (http://cleamc11.vub.ac.be/MEMETSY.html), let me remind you that the deadline for submission is March 10, that is, next week. (the deadline for the session on Semiotics of Autonomous Information Systems has already passed).

Some of you may remember the symposium on "The Evolution of Complexity", organized by PCP in 1995 in Brussels (see http://cleamc11.vub.ac.be/EINMAGSY.html). The most important papers presented at this symposium, plus a few other selected papers, have been bundled into a book. This volume (the "violet book" in the 8 volume series Proceedings of the interdisciplinary conference "Einstein meets Magritte", see http://cleamc11.vub.ac.be/CLEA/publications.html) has finally been typeset, and proofs have been sent for correction to the authors. This means that in a few months, it should be available from the publisher, Kluwer Academic.

Since we regularly get questions about the existence of study programs in the domain of cybernetics and systems, it seems worth noting the organization of the 5th European School of Systems Science, in Neuchatel, Switzerland, Sept. 7-11, 1998 (see http://www.unine.ch/CIESYS/ECOLE.html), although this is independent of PCP.

## News- March/April 1998

The editorial board of the Principia Cybernetica Project has been preparing its annual board meeting, which will take place this year in Santa Fe, New Mexico, around August 10-20. This meeting will take place together with an informal, invited workshop involving, in addition to the PCP board, some people from the Santa Fe Institute, Los Alamos National Laboratory and the New Mexico State University. The topic is "Emergent Semantic and Computational Processes in Distributed Information Systems" (see http://www.c3.lanl.gov/~joslyn/pcp/workshop98.html). This ties in with our work on self-organizing networks and the global brain.

The Brussels PCP group has received an extensive visit by Mark Bickhard, a cognitive scientist/philosopher/psychologist from Lehigh University, where he has been working with the recently deceased Donald T. Campbell, a PCP associate. Bickhard has developed a philosophy very close to the one we call "Metasystem Transition Theory". Starting from a process ontology, Bickhard develops the theme of variation and selection and emergent organization at the different levels of reality, from quantum fields, via crystals, to living organisms and knowledge, with a specific emphasis on persons. His "interactivist" theory of representation is very close to our view of knowledge based on models, where correspondence is replaced by construction, constrained by selection on the basis of predictions. Bickhard is likely to join the project as an "associate". More info about his work is available at his home page: http://www.lehigh.edu/~mhb0/mhb0.html

The full program of the symposium on memetics, organized by PCP and the Journal of Memetics, including the abstracts of all accepted contributions is now available on the web: http://cleamc11.vub.ac.be/MEMETSY.html. Some 23 contributions have been selected for presentation.

## News - May/June 1998

Our plans for the annual board meeting of PCP in Santa Fe, New Mexico, have become more concrete. Johan Bollen, Alex Riegler, Cliff Joslyn and Francis Heylighen will meet during the period August 1 to 20, and will be joined by Valentin Turchin from August 9. The accompanying workshop on "Emergent Semantic and Computational Processes in Distributed Information Systems" (see http://www.c3.lanl.gov/~joslyn/pcp/workshop98.html) on August 10-11 is now taking concrete shape, with most abstracts available on the web. This workshop wil hopefully be the start for a fruitful collaboration between PCP and the "Symbiotic Intelligence Project" (http://ishi.lanl.gov/symintel.html), which groups researchers from Los Alamos National Laboratory and Santa Fe Institute. The subject would be the application of self-organizing systems to support collective intelligence on the web.

The Brussels PCP group has received another extensive visit, this time by Liane Gabora, an artificial life/memetics researcher from UCLA, and member of the editorial board of the Journal of Memetics. There is a good chance that she will join us to do research at the Center "Leo Apostel" (CLEA) on the emergence of culture during evolution. Liane has developed an "autocatalytic" model for the emergence of culture or thought, inspired by Stuart Kauffman's work on the origin of life and sparse distributed memory models of cognition. This fits in both with PCP's theory of metasystem transitions, and CLEA's project on "transitions between hard and soft layers of reality". More info on her work can be found at http://cleamc11.vub.ac.be/CLEA/seminars/Gabora.txt . A recent paper is available at http://www.cpm.mmu.ac.uk/jom-emit/1997/vol1/gabora_l.html

Most papers to be presented at the symposium on memetics, organized by PCP and the Journal of Memetics, are now available on the web: http://cleamc11.vub.ac.be/MEMETSY.html. The symposium will take place for two and a half days, from August 26 (afternoon) to August 28. The latest issue (June) of the Journal of Memetics has been published at http://www.cpm.mmu.ac.uk/jom-emit/1998/vol2/index.html

The project on progress in global quality of life which we submitted was unfortunately not accepted by the funding agency. Neither was Alex Riegler's application for a 3 year Postdoc research contract at CLEA. We'll have to try again next year, or find alternative sources of funding. Johan Bollen has carried out extensive psychological experiments, in collaboration with people from the Catholic University of Leuven, to test the basic assumptions that underly our "learning web" methodology (http://cleamc11.vub.ac.be/LEARNWEB.html). At first sights, the results seem positive, but the data need much further processing.

After a year of relatively low level activity, the discussions on our PRNCYB-L mailing list have become very intensive again. Especially the topics of "non-physical experience", "mind and body", "reductionism, holism and complexity" and "will and free will" have produced dozens of messages each. John J. Kineman is presently exploring the possibility to create a two-way gateway between the PRNCYB-L emailing list, and a HyperNews discussion system on the web, that could be used also by non-PRNCYB subscribers. HyperNews was originally developed by another PRNCYB member, Daniel LaLiberte. You can try out a first prototype at http://HyperNews.ngdc.noaa.gov/HyperNews/get/ecosci/1.html

## News - July/August 1998

BOARD MEETING IN NEW MEXICO

PCP has had had a successfull annual meeting of the editorial board in Santa Fe, New Mexico, in which outstanding issues were discussed, and contacts were made with different researchers working in New Mexico.

The meeting was organized together with a workshop on "Emergent Semantic and Computational Processes in Distributed Information Systems" at the Los Alamos National Laboratory (LANL), in which all PCP visitors and local residents participated, together with a number of researchers from LANL, the Santa Fe Institute (SFI), and New Mexico State University (NMSU). The workshop was well attended and aroused quite some interest and discussion about the newly emerging domain of self-organization and complexity models applied to information networks, such as the web.

Texts of the contributions are being collected, and will be gradually made available on the workshop's website (http://www.c3.lanl.gov/~joslyn/pcp/workshop98.html). Afterwards, workshop proceedings will be published, most likely as a LANL internal report at first, and as a book or special issue of a journal in a second stage. This second stage is likely to propose a selection of the most relevant papers, rewritten to take into account the other contributions, together with some newly invited papers from people who did not attend the workshop but who are experts in the domain.

In addition, the PCP group had several interesting discussions with researchers working in the New Mexico area, including Norman Johnson, the driving force behing the LANL "Symbiotic Intelligence Project" (http://ishi.lanl.gov/symintel.html), John Casti from SFI, who was interested to publish a report of the workshop in the journal "Complexity" which he edits, Eric Bonabeau, another SFI resident and editor of "Complex Systems", who is a world authority on the collective intelligence exhibited by insect societies, and Liane Gabora, a memetics researcher affiliated to UCLA.

Liane confirmed that she has accepted our invitation to come to work in Brussels on a two year research contract, starting on Oct. 1. This will allow her to finish her PhD and continue her research on the emergence of culture, while collaborating with the Brussels PCP group at the Center "Leo Apostel". Inversely, a possibility was discussed for Johan Bollen from the Brussels group to come and work at LANL for a one year period, in order to collaborate more closely with Luis Rocha and Cliff Joslyn, the representatives of PCP in New Mexico. Other possibilities for collaboration were discussed with Bonabeau and Johnson, although no concrete decisions have been made as yet.

Because of these different side-activities, together with the not-to-be missed opportunities for sight-seeing and hiking in the spectacular New Mexico surroundings, we had perhaps not as much time for the board meeting itself as we had hoped. In particular, we did not manage to have in-depth discussions on fundamental theoretical issues, although we did discuss some interesting implications of the emergence of collective intelligence in animal and human societies for developing a more detailed model of large scale metasystem transitions. On the other hand, the meeting concluded with a long list of concrete plans for the further development of the project organization in general, and PCP web in particular. These objectives are summarized below.

On the organizational level, it was decided to create an American office for the project ("PCP West"), to complement the present European office in Brussels. This permanent PCP presence at LANL has been made possible by the recent promotion of PCP editor Cliff Joslyn to a tenured "staff" position at the Los Alamos lab. The PCP editorial assistants, Johan Bollen and Alex Riegler, were formally 'promoted' to "assistant editors". We also reiterated our aim to more closely involve the different "associates" of the project in the writing of nodes, and suggested some names of new people to invite as associates or authors of nodes. Finally, we decided to renew contacts with the International Society for Systems Science, which through Bela A. Banathy expressed their interest to collaborate with PCP.

PLANS FOR PCP-WEB

During the meeting we received a final confirmation from Mick Ashby, grandson of the famous cybernetician W. Ross Ashby, that he had received permission from the publishers of his grandfather's classic book, "Introduction to Cybernetics", to publish the book in an electronic version on PCP Web. Since this will be the third "out of print" book which we republish on the Web, we decided to create a special "library" section on PCP web, with electronic versions of important books. The Ashby book will be scanned in during the coming weeks and converted to PDF and HTML for easy printing and browsing.

Moreover, we decided to produce an easy-to-print "book" version of all PCP nodes, so that people don't need to browse between the hundreds of nodes, but can read a more or less complete version of PCP web on paper. A more ambitious aim, which may not be realized soon, is to provide PCP web users with a "shopping basket", in which they can gather a list of only those nodes ("pages") they are interested in, and then receive all these nodes at once in an easy-to-print file format, without the navigational formatting (menu bar, child nodes, etc.) that is only useful for web browsing.

PCP web itself is scheduled for a major overhaul, to improve both its organization and its appearance, so that it would become more easy to browse and to edit. Structurally, the idea is to clearly distinguish all database fields (author, date, title, content, etc.) within the HTML code, by introducing new tags such as Name. These should if possible comply with the newly emerging XML standard, which proposes and open-ended extension to HTML. In particular, a new field will be created for the "synopsys" (summary or definition) of a node. This new representation will make it much easier to reorganize and edit the whole of the web. "Modularizing" the separate entries that make up a node should also make it easier to change the layout of PCP-web.

We have been experimenting with a number of new layouts, which should be both esthetically pleasing and help the user to navigate more efficiently. We would be curious to hear your reactions and suggestions with respect to these different options. Some trial layouts can be seen at the subsequent URLs http://cleamc11.vub.ac.be/layout/Default1.html, ..., Default12.html. Apart from purely esthetical issues such as color schemes, icons and logos, the main issue is whether we should put the navigational structure of parent ("up") and child ("down") nodes in a vertical side bar (e.g http://cleamc11.vub.ac.be/layout/Default7.html), or in a horizontal box at the bottom of the page (e.g. http://cleamc11.vub.ac.be/layout/Default12.html). Please let us know which features or layouts you prefer.

For PCP web as a whole, it was decided to create, together with the New Mexico office, a New Mexico mirror of the main Brussels server, so as to facilitate access from America and provide a permanently available backup in case of server problems. Moreover, if a NSF proposal submitted by the LANL group and a group at NMSU would be accepted, money would become available to create a "collaborative knowledge space", at NMSU. This would contain an experimental version of PCP Web, the SFI web and perhaps others, so as to allow experiments with different algorithms for web self-organization or information retrieval, as they were developed by Luis Rocha, Johan Bollen and other PCP contributors. It was also decided to try and reserve some alternative domain names for the PCP server(s), in particular: pcp.vub.ac.be, pcp.lanl.gov and cybernetica.org (pcp.org, pcp.com and pcp.net are already taken by organizations that have nothing to do with Principia Cybernetica).

MEMETICS SYMPOSIUM IN NAMUR

The Brussels PCP people had hardly recovered from the jetlag of the journey back to Europe, or we had to go to the 3-yearly Cybernetics congress in Namur, Belgium, for the first ever symposium on Memetics. The symposium was organized and chaired by Mario Vaneechoutte and myself. It was quite successful, with attendance ranging between 15 and 40 people during the two and a half days. The discussions after each talk were particularly animated, showing that memetics has developed into a topic that receives a lot of interest, especially among young researchers. The average age of the contributors was quite low (most of them did not have a PhD yet), and about a generation younger than the age of the attendant to other symposia. The congress president, Jean Ramaekers, told me that he was very happy with this "rejuvenation" of a congress that has taken place without interruption since 1956.

By the way, Jean Ramaekers and me also discussed the possibility to create a Belgian association for cybernetics and systems science. This informal association, for which WOSC president Robert Vallee suggested the name "Systemica Belgica", would be used as a communication channel between researchers in Belgium, to inform each other about cybernetics related activities, such as seminars, conferences, projects, etc. If you work in or around Belgium and would be interested to participate, please send me a message with your address, domain of interest and suggestions about the organization.

From the scheduled symposium program (http://cleamc11.vub.ac.be/MEMETSY.html), only 3 contributors did not make it: Liane Gabora, who had apologized because she was too busy preparing her long term visit to Belgium, Thomas Quinn and Koen Bruynseels. On the other hand, a guy whose name I don't remember (?Rosdeitcher?) presented an improvised, but entertaining talk in which he sketched his own "conversion" from being a follower of Ayn Rand's "objectivist" philosophy to becoming an advocate of the memetic/cybernetic paradigm.

From the other, scheduled talks, I particularly appreciated my co-chair Mario Vaneechoutte's speculations on the origin of life as a model for memetics, Michael Best's simulation of cultural vs. genetic evolution, Szabolcs Szamado's analysis of fundamental memetic replication processes, John Evers' application of memetics to explain altruism and Paul Marsden's review of research on "social contagion" as an existing body of empirical data that cries out for a memetic reinterpretation. The talk by my PCP collaborator Johan Bollen about our learning web algorithms also generated a very positive response, although I am of course not in an objective position to judge about its quality (;-). Practically all papers should by now be available on the web via the above symposium URL. They will be published by the end of this year as part of the congress proceedings.

The symposium was concluded with a lively panel discussion, chaired by Gary Boyd, in which the absent panel member Gabora was replaced by Paul Marsden, and a short brain storming session with all remaining participants to generate a list of suggestions for us to advance the field of memetics. One of the concrete decisions was to steer the Journal of Memetics more in the direction of the system of commentary used by "Behavioral and Brain Sciences". This requires us setting up a list of commentators.

# First public introduction of the Principia Cybernetica Project

Author: Cliff Joslyn, and Valentin Turchin,
Updated: 23 Dec 89
Filename: FPUBINT.html

Date:         Sat, 23 Dec 89 12:41:22 EDT
Sender:       Cybernetics and Systems
From:         CYBSYS-L Moderator
Subject:      Introduction to the 'Principia Cybernetica' Project
Really-From: cjoslyn@bingvaxu.cc.binghamton.edu (Cliff Joslyn)
[ NB: The following is available from the file server under the name
CYBPROJ REP.  Moderator ]

_INTRODUCTION TO THE PRINCIPIA CYBERNETICA' PROJECT_
Cliff Joslyn and Valentin Turchin
Copyright 1990 Cliff Joslyn and Valentin Turchin

BRIEF DESCRIPTION OF THE PROJECT
The "Principia Cybernetica" project is an attempt by a group of
researchers to build a complete and consistent system of philosophy.
organizers as "cybernetic", issues of general philosophical concern,
including epistemology, metaphysics, and ethics, or the supreme human
values.
This philosophical system will not be developed as a traditional
document, but rather as a _conceptual network_.  A unit, or node, in
the network can be a book, a chapter, a paragraph, a definition, an
essay, an exposition on a topic, a picture, a reference, etc.  Using
this structure, multiple hierarchical orderings of the network will
be maintained, allowing giving both readers and authors flexible
access to the whole system.  The network will be implemented in a
hybrid computer-based environment involving Hypertext, Hypermedia,
electronic mail, and electronic publishing [Joslyn 1990].
Development of this phiosophy is seen as a very long-term project
involving many participants supervised by an Editorial Board.  While
publication of traditional documents by individual authors or small
groups will be made periodically, the project is seen as necessarily
open-ended and developing, essentially a process or discourse among a
community of researcher.
The organizers have in mind not only a process of discourse about
cybernetic philosophy, but also already have established a strong
basis for the _content_ of such a philosophy [Turchin and Joslyn
1989].  But the form of the development should be such as to enable
other opinions to be incorporated.

KEY CRITERIA FOR THE PROJECT
The following is a partial list of desiderata for the "Principia
Cybernetica" project:
1. For a group of researchers, perhaps not all geographically close,
to collaboratively develop a system of philosophy, where
philosophy is taken in the general sense of clear and consistent
system according to their role in the project development;
3. To produce a system of philosophy that can develop dynamically
over time, with continuing refinement and expansion;
4. For the system of philosophy to fully reflect and incorporate the
semantic relations inherent among the terms being explicated;
5. To allow the explication of terms and senses of terms, and to
unify and synthesize notations and terminology among researchers
in different disciplines;
6. To support the process of argument and dialog among experts
toward the end of consensus at the level of the meanings of
words and the relations among those meanings;
7.  To support the publication of intermediate and final stages of
parts or the whole of the philosophical system;
8. To support bibliographical and historical reference;
9.  To support mathematical notation and the easy movement among
natural language, formal language, and mathematics;
10. To allow researchers to develop or read the philosophical system
in various orders and in various degrees of depth or specificity;
11. To allow access to the system for both participants who wish to
author and users who wish to read, browse, or study;
12. To support the publication of various special-purpose documents,
including dictionaries, encyclopedias, texts on a subject,
reference pages, essays, dialogs on a subject, or "streams of
consciuosness";
13. To allow the representation and utilization of knowledge in both

ON SEMANTIC ANALYSIS AND CONSENSUS BUILDING
This project will aim at _building consensus_, not by normatively
establishing a monolithic edifice, but through the explication of the
various senses of terms through careful semantic analysis of words and
concepts used in systems and cybernetics in the context of their
historical development.
While we hope that actual progress can be made through the elimination
of incoherent or anachronistic usages, it may be that a simple listing
of the various senses will be required.  If one contributor asserts
"P", and another "not P", and no further progress can be made, then in
the worst case a kind of "null consensus" can be achieved by including
"P or not P" in the project.  For example, the concept of "information"
is sometimes described as "high entropy", other times as "low
entropy".  At the very least the different conditions under which these
usages arise should be described.  At best one usage would be
eliminated.

MANAGEMENT OF THE PROJECT
The organizers of the project are Valentin Turchin (Computer Science,
City College of New York, CUNY) and Cliff Joslyn (Systems Science,
SUNY-Binghamton).  Together they constitute the current Board of
Editors for the project, and are actively looking for like-minded
researchers to share in that responsibility.  The Board is responsible
for implementation of the system and the collection and development of
the material.  Similar to a journal, it may rely on an Editorial
Advisory Board, and other associated editors, referees, contributors,
and critics.
Nodes of the project will be in one of the following categories:

in common by the contributors and the Editorial Board.
2)  _Individual Contribution Nodes_: Further development of the
ideas expressed in the Consensus Nodes at greater depth.
This development need not be held consensually by the
contributors and Editors, but should be similar in spirit and
style to the Consensus Nodes.
3)  _Discussion Nodes_: Including defence or criticism of the
consensus or individual contribution nodes and development of
other ideas.
It is critical for the success of the project that a number of experts
work cooperatively towards its success.  The intent is to help unify
and synthesize the relatively fragmented systems and cybernetics
conceptual territory by grounding terms in a common consensual basis.

Joslyn, Cliff: (1990) "The Necessity of a New Tool for Philosophical
Development", to be published
Turchin, Valentin and Joslyn, Cliff: (1989) "The Cybernetic Manifesto",
to be published 

# Progress Report: 5 years of PCP

Author: F. Heylighen, C. Joslyn,
Updated: Oct 11, 1994
Filename: PROGREP.html

PCP was publically introduced in December 1989 with a first general proposal, containing a list of goals. Since these objectives were formulated rather explicitly, point by point, it is easy to now check in how far they have become reality. Although these goals sounded very ambitious at the moment, the funny thing is that most of them have effectively been realized.

We have a lot of it to thank to the World-Wide Web system becoming available in the meantime, solving most of our technical problems. We certainly must give credit to Cliff Joslyn for so well anticipating a system which at that moment did not exist, except in the mind of its inceptor, Tim Berners-Lee, who wrote his first, privately circulated, proposal for WWW at about the same time.

We will now review each goal separately, by quoting each of the 12 points from the original document, noting "YES" it it has been achieved, "NO" if we aren't there yet, and "MAYBE" if it has been achieved partially.

The following is a partial list of desiderata for the "Principia Cybernetica" project:

1. For a group of researchers, perhaps not all geographically close, to collaboratively develop a system of philosophy, where philosophy is taken in the general sense of clear and consistent language about ideas and concepts

YES. The system of philosophy is there (albeit far from finished), partially implemented over the WWW server, and its developers have been working collaboratively in spite of large geographical obstacles (the Atlantic Ocean among other things).
2. To allow these researchers different levels of access to the system according to their role in the project development
YES. The present WWW server allows editors to read and edit all material via passwords, the different contributors to read most material, and to make annotations. Soon they will be able to edit their own annotation (but not other people's)
3. To produce a system of philosophy that can develop dynamically over time, with continuing refinement and expansion
YES. The quasi-hierarchical hypertext structure of linked nodes makes it easy to add or refine concepts and principles, while maintaining a stable core.
4. For the system of philosophy to fully reflect and incorporate the semantic relations inherent among the terms being explicated
Mostly NO. Some semantic relations have been made explicit in a system of typed links for certain nodes, but this is basically an unfinished experiment.
5. To allow the explication of terms and senses of terms, and to unify and synthesize notations and terminology among researchers in different disciplines
Mostly NO. There is some explication of terms and senses of terms (e.g. in the Glossary), but it is far from systematical, and we certainly haven't as yet achieved any terminological unification.
6. To support the process of argument and dialog among experts toward the end of consensus at the level of the meanings of words and the relations among those meanings
MAYBE. PRNCYB-L and the annotations on the server, as well as more traditional meetings and publications can certainly be said to "support" argument and dialog, but the end of consensus is still quite far away.
7. To support the publication of intermediate and final stages of parts or the whole of the philosophical system
YES. Several papers have already been published, and the hierarchical organization of the conceptual network should make it relatively easy to take out parts or the whole and publish them as a book, paper or report.
8. To support bibliographical and historical reference
YES. In a hypertext system this is almost trivial. Just make a link to a node with a historical review or a list of publications. The actual lists of publications and historical overviews are virtually non-existent though.
9. To support mathematical notation and the easy movement among natural language, formal language, and mathematics
Mostly NO. A shortcoming of the HTML markup language for WWW is that as yet it does not provide easy ways to express mathematical notations, though that should be facilated in the future (e.g. by automatic HTML<->TEX conversions). The "easy movement" is rather vague, so it is difficult to conclude in how far it has been achieved.
10. To allow researchers to develop or read the philosophical system in various orders and in various degrees of depth or specificity
YES. The Principia Cybernetica Web can be read in many different orders, and readers can go in depth, or remain more on the surface while still getting a basic picture. More work needs to be done to fill in all the different levels, though.
11. To allow access to the system for both participants who wish to author and users who wish to read, browse, or study
YES. WWW now allows both reading of text and entering of new text through annotation.
12. To support the publication of various special-purpose documents, including dictionaries, encyclopedias, texts on a subject, reference pages, essays, dialogs on a subject, or "streams of consciuosness"
MAYBE. Several of these types of texts have been published, and as stated earlier it should be easy to take out parts of the Web, but it is not clear in how far the generation of these special purpose documents is really "supported".
13. To allow the representation and utilization of knowledge in both its breadth and its depth.
Probably YES. Representation in breadth and depth is certainly supported, but the rest is rather vague. It sounds well but it is not clear in how far PCP knowledge is being "utilized". If the number of people consulting the Web is a measure, it is certainly not being ignored, though one can doubt whether much is retained by the readers.

The overall score seems pretty impressive: if we give 1 for a YES, 0 for a NO and 0.5 for a MAYBE, we get 9 out of 13, that is: 69 %, after less than 5 years of development.

Note, however, that these are objectives mostly about the "form" or organization of the Project. The goals for the "content" or philosophy were never stated as explicitly, apart from building a "complete and consistent philosophical system", including metaphysics, epistemology and ethics. Our system is definitely not complete, though its reach is quite broad, and few fundamental issues are left untouched. Its consistency is debatable but there are no obvious contradictions.

Taking into account that the original proposal described PCP as a "very long term" project, we may conclude that there is reason to be proud of what we have achieved in a rather short term existence.

# The Cybernetic Manifesto

Author: V. Turchin, C. Joslyn,
Updated: Oct 1989
Filename: MANIFESTO.html

## 1.Philosophy

Philosophy is the putting of our thought and language in order. Philosophy is important. Philosophy is a part of our knowledge.

## 2.Knowledge

Cybernetic epistemology defines knowledge as the existence in a cybernetic system of a model of some part of reality as it is perceived by the system. A model is a recursive generator of predictions about the world which allow the cybernetic system to make decisions about its actions. The notions of meaning and truth must be defined from this perspective.

Knowledge is both objective and subjective because it results from the interaction of the subject (the cybernetic system) and the object (its environment). Knowledge about an object is always relative: it exists only as a part of a certain subject. We can study the relation between knowledge and reality (is the knowledge true or false, first of all); then the subject of knowledge becomes, in its turn, an object for another subject of knowledge. But knowledge in any form (a proposition, a prediction, a law), irrespective of any subject is a logical absurdity. A detailed development of cybernetic epistemology on the basis of these definitions is critical for the formalization of the natural science and natural philosophy, and the interpretation of mathematical systems.

## 3.Freedom, will, control

Cybernetic metaphysics asserts that freedom is a fundamental property of things. Natural laws act as constraints on that freedom; they do not necessarily determine a course of events. This notion of freedom implies the existence of an agency, or agencies, that resolve the indeterminacy implicit in freedom by choosing one of the possible actions. Such an agency is defined as a will. A will exercises control over a system when the freedom of that system is constrained by actions chosen by the will.

## 4.God

We understand God in the spirit of pantheism. God is the highest level of control in the Universe. God is for the Universe what human will is for human body. Natural laws are one of the manifestations of God's will. Another manifestation is the evolution of the Universe: the Evolution.

## 5.Metasystem transition

When a number of systems become integrated so that a new level of control emerges, we say that a metasystem has formed. We refer to this process as a metasystem transition.

A metasystem transition is, by definition, a creative act. It cannot be solely directed by the internal structure or logic of a system, but must always comes from outside causes, from "above".

## 6.Evolution

The metasystem transition is the quantum of evolution. Highly organized systems, including living creatures, are multilevel hierarchies of control resulting from metasystem transitions of various scales.

Major evolutionary events are large-scale metasystem transitions which take place in the framework of the trial-and-error processes of natural selection.

Examples include: the formation of self-duplicating macromolecules; formation of multicellular organisms; emergence of intelligent organisms; formation of human society.

## 7. Human intelligence

Human intelligence, as distinct from the intelligence of non-human animals, emerges from a metasystem transition, which is the organism's ability to control the formation of associations of mental representations. All of specifically human intelligence, including imagination, language, self-consciousness, goal-setting, humor, arts and sciences, can be understood from this perspective.

## 8.Social integration

The emergence of human intelligence precipitated a further, currently ongoing, metasystem transition, which is the integration of people into human societies. Human societies are qualitatively different from societies of animals because of the ability of the human being to create (not just use) language. Language serves two functions: communication between individuals and modeling of reality. These two functions are, on the level of social integration, analogous to those of the nervous system on the level of integration of cells into a multicellular organism.

Using the material of language, people make new --- symbolic - models of reality (scientific theories, in particular) such as never existed as neural models given us by nature. Language is, as it were, an extension of the human brain. Moreover, it is a unitary common extension of the brains of all members of society. It is a collective model of reality that all members of society labor to improve, and one that preserves the experience of preceding generations.

## 9.The era of Reason

We make a strong analogy between societies and neural, multicellular organisms. The body of a society is the bodies of all people plus the things made by them. Its "physiology" is the culture of society. The emergence of human society marks the appearance of a new mechanism of Universal Evolution: previously it was natural selection, now it becomes conscious human effort. The variation and selection necessary for the increase of complexity of the organization of matter now takes place in the human brain; it becomes inseparable from the willed act of the human being. This is a turning point in the history of the world: the era of Reason begins.

The human individual becomes a point of concentration of Cosmic Creativity. With the new mechanism of evolution, its rate increases manifold.

## 10.Global integration

Turning to the future we predict that social integration will continue in two dimensions, which we can call width and depth. On the one hand (width), the growth of existing cultures will lead to the formation of a world society and government, and the ecological unification of the biosphere under human control. The ethics of cybernetical world-view demands that each of us act so as to preserve the species and the ecosystem, and to maximize the potential for continued integration and evolution.

## 11.Human super-beings

On the other hand (depth), we foresee the physical integration of individual people into "human super-beings", which communicate through the direct connection of their nervous systems. This is a cybernetic way for an individual human person to achieve immortality.

## 12.Ultimate human values

The problem of immortality is the problem of ultimate human values, and vice versa.

Living creatures display a behavior resulting from having goals. Goals are organized hierarchically, so that in order to achieve a higher-level goal the system has to set and achieve a number of lower-level goals (subgoals). This hierarchy has a top: the supreme, ultimate goals of a creature's life. In an animal this top is inborn: the basic instincts of survival and reproduction. In a human being the top goals can go beyond animal instincts. The supreme goals, or values, of human life are, in the last analysis, set by an individual in an act of free choice. This produces the historic plurality of ethical and religious teachings. There is, however a common denominator to these teachings: the will to immortality. The animal is not aware of its imminent death; the human person is. The human will to immortality is a natural extension of the animal will for life.

## 13.Decline of metaphysical immortality

One concept of immortality we find in the traditional great religions. We designate it as metaphysical. It is known as immortality of soul, life after death, etc. The protest against death is used here as a stimulus to accept the teaching; after all, from the very beginning it promises immortality. Under the influence of the critical scientific method, the metaphysical notions of immortality, once very concrete and appealing, are becoming increasingly abstract and pale; old religious systems are slowly but surely losing their influence.

## 14.Creative immortality

Another concept of immortality can be called creative, or evolutionary. The idea is that mortal humans contribute, through their creative acts, to the ongoing universal and eternal process -- call it Evolution, or History, or God -- thus surviving their physical destruction. This uniquely human motive underlies, probably, all major creative feats of human history.

## 15.Cybernetic immortality

The successes of science make it possible to raise the banner of cybernetic immortality. The idea is that the human being is, in the last analysis, a certain form of organization of matter. This is a very sophisticated organization, which includes a high multilevel hierarchy of control. What we call our soul, or our consciousness, is associated with the highest level of this control hierarchy. This organization can survive a partial --- perhaps, even a complete --- change of the material from which it is built. It is a shame to die before realizing one hundredth of what you have conceived and being unable to pass on your experience and intuition. It is a shame to forget things even though we know how to store huge amount of information in computers and access them in split seconds.

## 16.Evolution and immortality

Cybernetic integration of humans must preserve the creative core of human individual, because it is the engine of evolution. And it must make it immortal, because for the purpose of evolution there is no sense in killing humans. In natural selection, the source of change is the mutation of the gene; nature creates by experimenting on genes and seeing what kind of a body they produce. Therefore, nature has to destroy older creations in order to make room for the newer ones. The mortality of multicellular organisms is an evolutionary necessity. At the present new stage of evolution, the evolution of human-made culture, the human brain is the source of creativity, not an object of experimentation. Its loss in death is unjustifiable; it is an evolutionary absurdity. The immortality of human beings is on the agenda of Cosmic Evolution.

## 17.Evolution of the human person

The future immortality of the human person does not imply its frozen constancy. We can understand the situation by analogy with the preceding level of organization.

Genes are controllers of biological evolution and they are immortal, as they should be. They do not stay unchanged, however, but undergo mutations, so that human chromosomes are a far cry from the chromosomes of primitive viruses.

Cybernetically immortal human persons may mutate and evolve in interaction with other members of the super-being, while possibly reproducing themselves in different materials. Those human persons who will evolve from us may be as different from us as we are different from viruses. But the defining principle of the human person will probably stay fixed, as did the defining principle of the gene.

## 18. How integration may occur

Should we expect that the whole of humanity will unite into a single super-human being?

This does not seem likely, if we judge from the history of evolution. Life grows like a pyramid; its top goes up while the basis is widening rather than narrowing. Even though we have seized control of the biosphere, our bodies make up only a small part of the whole biomass. The major part of it is still constituted by unicellular and primitive multicellular organisms, such as plankton. Realization of cybernetic immortality will certainly require some sacrifices --- a vehement drive to develop science, to begin with. It is far from obvious that all people and all communities will wish to integrate into immortal super-beings. The will to immortality, as every human feature, varies widely in human populations. Since the integration we speak about can only be free, only a part of mankind -- probably a small part - should be expected to integrate. The rest will continue to exist in the form of "human plankton".

## 19.Integration on the Cosmic scene

But it is the integrated part of humanity that will ultimately control the Universe. Unintegrated humanity will not be able to compete with the integrated part. This becomes especially clear when we realize that the whole Cosmos, not the planet Earth, will be the battlefield. No cosmic role for the human race is possible without integration. The units that take decisions must be rewarded for those decisions, otherwise they will never take them. Can we imagine "human plankton" crowded in rockets in order to reach a distant star in ten, twenty or fifty generations? Only integrated immortal creatures can conquer the outer space.

## 20.Current problems

At present our ideas about the cybernetic integration of humans are very abstract and vague. This is inevitable; long range notions and goals may be only abstract. But this does not mean that they are not relevant to our present concerns and problems. The concept of cybernetic immortality can give shape to the supreme goals and values we espouse, even though present-day people can think realistically only in terms of creative immortality (although -- who knows?).

The problem of ultimate values is the central problem of our present society. What should we live for after our basic needs are so easily satisfied by the modern production system? What should we see as Good and what as Evil? Where are the ultimate criteria for judging social organization?

Historically, great civilizations are inseparable from great religions which gave answers to these questions. The decline of traditional religions appealing to metaphysical immortality threatens to degrade modern society. Cybernetic immortality can take the place of metaphysical immortality to provide the ultimate goals and values for the emerging global civilization.

## 21.Integration and freedom

We are living at a time when we can see the basic contradiction of the constructive evolution of mankind very clearly: it is the contradiction between human integration and human freedom. Integration is an evolutionary necessity. If humanity sets itself goals which are incompatible with integration the result will be an evolutionary dead end: further creative development will become impossible. Then we shall not survive. In the evolving Universe there is no standstill: all that does not develop perishes. On the other hand, freedom is precious for the human being; it is the essence of life. The creative freedom of individuals is the fundamental engine of evolution in the era of Reason. If it is suppressed by integration, as in totalitarianism, we shall find ourselves again in an evolutionary dead end. This contradiction is real, but not insoluble. After all, the same contradiction has been successfully solved on other levels of organization in the process of evolution. When cells integrate into multicellular organisms, they continue to perform their biological functions--metabolism and fission. The new quality, the life of the organism, does not appear despite the biological functions of the individual cells but because of them and through them. The creative act of free will is the "biological function" of the human being. In the integrated super-being it must be preserved as an inviolable foundation, and the new qualities must appear through it and because of it. Thus the fundamental challenge that the humanity faces now is to achieve an organic synthesis of integration and freedom.

Author: F. Heylighen, V. Turchin,
Updated: Jul 19, 1994
Filename: REACT.html

The following is a list of reactions and criticisms in different places of the Principia Cybernetica Project and its Web server. See also: User Annotations.

# Criticisms of Principia Cybernetica

Author: F. Heylighen & C. Joslyn
Updated: Aug 1993
Filename: CRITIC.html

Turchin an Joslyn originally announced Principia Cybernetica by posting a first general proposal, and "The Cybernetic Manifesto on the CYBSYS-L electronic mailing list in the autumn of 1989 (see the Project history). This led to a a sometimes very heated debate. The most outspoken critic was Joseph Goguen, who interpreted the use of concepts like "control", "hierarchy" and "integration" as signs of a dangerous, totalitarian ideology. Joslyn and Turchin reacted by stressing the essential role human freedom plays in the philosophy, and by remarking that terms like control and hierarchy should be understood primarily in their abstract, technical sense. In fact, the metasystem transition, where a new control level emerges, should be seen as an increase, rather than a decrease, of the freedom of the system. This criticism led to a deeper understanding of the necessity for careful articulation of the ideas behind Principia Cybernetica, in the hope of avoiding misinterpretation.

Goguen also opposed the striving towards consensus, which is a fundamental goal of the Principia Cybernetica, on the grounds that all opinions are valuable, and that no one viewpoint should be privileged. This criticism is more difficult to answer in a few words.** It was repeated in different forms by different people, mostly those with a "post-modernist" or "social constructivist" philosophy. These critics stress the relativity of knowledge, and the creativity which arises from a variety of different opinions.

But we hold that this creativity can only appear through a confrontation and conversation between the different opinions, and that is just what Principia Cybernetica proposes. Without at least an attempt to reach consensus, people will stick to their own opinions, and no novelty is created. But it is not our intention to impose a consensus, and we start from the principle that Principia Cybernetica must be open-ended: every new idea or opinion can be incorporated somewhere along the way, even if only as a "discussion node". We do not expect to reach a complete consensus in any foreseeable future. Yet we do hold that there is a deep unity in the ideas characterizing Systems Theory and Cybernetics. In our experience, those with a background in Cybernetics or Systems share these fundamental concepts and values, although they may express them with different words. Further, we hold that a fundamental, broad consensus at the conceptual level is necessary for the advancement of a discipline, or a society.

Other criticisms (albeit in a generally sympathetic spirit) about the philosophy behind Principia Cybernetica, and its practical realizability, were made by Gerard de Zeeuw and Rod Swenson to Heylighen when he presented the ideas of Principia Cybernetica to a number of people at the European Meeting on Cybernetics and Systems in Vienna (April 1990). Swenson mentioned in particular the difficulty of maintaining copyright in a network which is authored collectively by many different people. On the other hand, Principia Cybernetica was enthusiastically welcomed by Gordon Pask, who is one of the main theorists in the "social constructivist" paradigm, and the creator of conversation theory.

# Principia Cybernetica Web and the "Best of the Web" awards

Author: F. Heylighen,
Updated: Jul 19, 1994
Filename: BESWEB.html

Principia Cybernetica Web has participated in the 1994 "Best of the Web" awards, an international competition for the best services on the World-Wide Web. PCP Web was originally nominated for the category "Document Design" with the following quotation:

Principia Cybernetica Web

Dr. Francis Heylighen et al, Free U. of Brussels

Now this is hypertext! Over 600 documents of profuse hypertext. Several methods of navigating the library, including menus, indexes, and a graphical browser. The subject matter of cybernetics is very closely related to the Web itself.

After the free voting procedure, PCP Web ended up with a "Honorable Mention" for the "Document Design" category. The award in this category went to "Travels with Samantha", a narrative with lots of splendid color photographs and an interesting story, but nothing much in the area of hypertext design.

# Principia Cybernetica in "Wired" magazine

Author: Wired
Updated: Jul 19, 1994
Filename: REVWIR.html

The following is a quote from WIRED, a popular magazine devoted to cyberspace. (WIRED: San Francisco, Wired Ventures ltd., nr. 2.08, August 1994, p. 119)

## When Science Meets Net.Society.

The Principia Cybernetica Project is an attempt to unify systems theory and cybernetics, using the tools and methods of cybernetics itself. Managed by leading researchers from the City University of New York, NASA, and the Free University of Brussels, Principia Cybernetica is amassing an awesome and ever-growing info-tube of information on the underlying meme-technology of the Matrix: self-organizing systems, cybernetics, human-computer interaction, knowledge structures, cognitive science, artificial intelligence, philosophy, and evolution, to name a few. No ivory towers here, simply practical information on web-weaving and Internet use commingled with academic papers and cyberculture rants, such as Ronfeldt's Cyberocracy.

The websmiths are hard at work on this one, using a full bag of HTML (Hypertext Markup Language) tricks to bring you representational maps of the Webspace that you can click on, searchable indices, and other goodies. Choice tidbits include the realtime Web visualizer tools, John December's comprehensive treatise on computer-mediated communication, the full text of Darwin's On The Origin Of Species, and fresh news from the Project Xanadu folks, complete with an impressive bibliography. Start your education on the tech behind the hype at http://cleamc11.vub.ac.be/.

# References to Principia Cybernetica in different servers

Author: F. Heylighen,
Updated: Jul 23, 1998
Filename: REFSPCP.html

The Principia Cybernetica Web is being linked to by more and more other Web pages. You can get a complete list of the about 5000 sites linking to PCP Web from the AltaVista catalog. Many of them only mention the name of the project as anchor, or include some of our own characterizations of the project, but some of them also add a personal evaluation. (see also the review in Wired magazine, and the nomination for the Best of the Web Awards)

The following quotations give a good idea of how Principia Cybernetica Web is perceived by others. I have separated "personal" comments (made in various places linking to PCP Web because they find the subject interesting) from "professional reviews" by various web surveying service, which are not a priori interested in the subject, but try to evaluate PCP web for the "general reader" who is unlikely to know anything about cybernetics.

You can find further references to PCP yourself via the Altavista search engine.

## Personal appreciation

"Principia Cybernetica Project. Philosophy on the Net. As only could be done on the Net. This is complex but amazing." (Cool links)

"an excellent resource for cybernetics, systems theory, and other disciplines often mentioned in Neuro-Linguistic Programming circles" (Neuro-Linguistic Programming and Design Human Engineering )

"The most impressive Cybernetics resource I've ever seen." (Intelligent Systems )

"An extraordinarily interesting site about evolution, cybernetics and philosophy." (Evolution Resources on the Internet)

"... for easy to understand definitions of cybernetics and virtual reality" (Virtual Reality Research)

"The stated goal of the Principia Cybernetica Project is to link all of Mankind's knowledge... have to give them credit for ambition." (Recommended Internet Resources)

"An intriguing place" (http://www.honors.indiana.edu/docs/interest.html)

"Everything you ever wanted to know about systems and cybernetics".(Web Sites With Information on Systems Science Topics)

"congratulations on a job well-done, both in initiating Principia Cybernetica in the first place, and also in implementing the Principia Cybernetica Web, which is a rare and rich site on the Net." (Arkuat's comments on PCP )

"...and finally, the ultimate...
Principia Cybernetica" (Onar Aam's Related Servers page)

"An extremely sensible philosophy. The next step in human evolution!" (Expanding Your Consciousness )

" For some theoretical background try [...] the Principia Cybernetica Web which will stretch your mind a little" (Media lab). "an excellent site" (Wiener: ideas)

" This is a remarkable site: Systems Science, Feedback Loops, etc. It's a very busy site, so be patient and keep trying." (Quality related Information Sources)

" More than you can ever learn on cybernetics and related issues - the Website is overwhelming! (Frontier organizations on the Web)

"The Principia Cybernetica site is the largest Web 'nexus' on cybernetics and general systems theory."(Guide to Autopoiesis related Internet Resources)

"a fascinating collection of indexes and links on a diverse range of topics collateral to Gestalt and Systems conceptualization" (Psychology and Psychotherapy sources)

"An extraordinary experience of collective creation on the Web : an international group of research scientists is writing an hypertext on the global brain created by the interconnection of men, computers and network. A quantum leap into the third millenium." (Joë de Rosnay's list of Web Future Sites)

"Un extraordinario proyecto que prentende lograr las respuestas a las preguntas fundamentales del ser humano a traves de la inteligencia artificial."(Teor*ias Generales de Avanzada)

"By now the alarm was permanently set at 4:30 am but Timothy Alan Hall never heard it. He had rewired the little clock radio to activate an ancient reel-to-reel recorder on the other side of the trailer, hooked up an equally old set of theatre speakers and had it rigged to play his tape recording of a vintage 8086 computer booting up off dual floppies, grinding into first gear with a seriously irritating reminder of days long gone.[...] Tim was perfectly aware of the existence of memetic evolution, an obvious competition going on between genes and memes and his favorite web site was Principia Cybernetica which he studied when time permitted. The various philosophical subjects and New Agey topics failed to hold his interest for long but the truth was that memes did exist, were powerfully motivated and his own life had been changing positively because of them. The truth was inescapable."(TRAP CITY: A Serial Story)

## Reviews by web services

"What can artificial systems tell us about the meaning of life? What are we doing here anyway? These are some of the questions tackled on the PCP's Web server at the Free University of Brussels." (Global Network Navigator)

"The cool, the innovative, the excellent - they're all here in our gallery of Internet resources that exemplify the pioneering spirit of the Internet. These are the pages we like this week: December 30, 1994 to January 5, 1995. Academia: Cybernetics and Systems Theory" (Netscape's "What's Cool" list)

"A 3 star site. Rating Summary:
Net Appeal: 8 [out of 10], Depth of Coverage: 8, Ease of Exploration: 8
Audience: Philosophers, Evolutionists, System Theorists
Description: The Principia Cybernetica Web is the home of a project that promotes the computer-supported collaborative development of an evolutionary-systemic philosophy. This project tackles philosophical questions with the help of cybernetic theories and technologies. Visitors to the site will find an overview of the plan and details of its metasystem transition theory, among others." (Magellan's review of the PCP site)

"Content: 41/50, Presentation: 34/50, Experience: 35/50
The Principia Cybernetica Project is an in-depth look at the whole of philosophical thought, and it's not for armchair thinkers. Using a system of "nodes," it attempts to fill in the whole of human philosophical thought, from ethics to physics to evolutionary theory. In its complete form, the authors maintain it will give you the answer to basic questions like "Who am I? Why am I here?" It's quite involving, though occasionally we'd be happy with the answer to "What the heck does this mean?" on some of these pages. " (Point's review of the PCP site)

"A platinum site: Overall Rating: 5 stars (out of 5), content: 5 stars, presentation: 4 stars, personality: 4 stars
The Principia Cybernetica Project brings together a number of scholars from around the globe to tackle age-old philosophical problems with the aid of modern cybernetic theories and technologies. If cyberphilosophy is your forte, you can delve into subtopics that include ethics, metaphysics, ontology, and many other disciplines identified by long words. Even people with less arcane interests will find the Web Dictionary of Cybernetics and Systems useful." (NETGUIDE'S BEST OF THE WEB, details on PCP site)

Congratulations! Your page: http://cleamc11.vub.ac.be/ has been selected to receive a Links2Go Key Resource award in the Philosophy topic. The Links2Go Key Resource award is both exclusive and objective. Fewer than one page in one thousand will ever be selected for inclusion. Further, unlike most awards that rely on the subjective opinion of "experts," many of whom have only looked at tens or hundreds of thousands of pages in bestowing their awards, the Links2Go Key Resource award is completely objective and is based on an analysis of millions of web pages. During the course of our analysis, we identify which links are most representative of each of the thousands of topics in Links2Go, based on how actual page authors, like yourself, index and organize links on their pages.

# Context of Principia Cybernetica

Author: C. Joslyn, F. Heylighen,
Updated: Aug 1993
Filename: CONTEXT.html

PCP as a collaborative attempt to integrate and found existing knowledge has predecessors in general intellectual history (Talmud, Adler), as well as in the history of systems science and cybernetics. In particular different similar attempts to build compendia or "encyclopedia-like" works can be mentioned, such as Krippendorf's Dictionary of Cybernetics, Singh's Systems and Control Encyclopedia, Charles François' Dictionary of Systems and Cybernetics, the work of Troncale and Snow in the context of the International Society for Systems Science, and the Glossary on Cybernetics and Systems Theory developed for the American Society for Cybernetics.

In mathematics, we can mention Whitehead and Russell's Principia Mathematica, and the Bourbaki group's cooperative work on the set-theoretic foundations of mathematics

# Principia Cybernetica-related research in systems and cybernetics

Updated: Aug 1993
Filename: PCPCYBS.html

[Node to be completed]

Powers

Campbell

Beyond 2nd Order cyb.

# Metasystem Transition Theory

Author: C. Joslyn, F. Heylighen, V. Turchin,
Updated: Jul 7, 1997
Filename: MSTT.html

Metasystem Transition Theory (MSTT) is the name we have given our particular cybernetic philosophy. Its most salient concept is, of course, the Metasystem Transition (MST), the evolutionary process by which higher levels of complexity and control are generated. But it also includes our views on philosophical problems, and makes predictions about the possible future of mankind and life. Our goal is to create, on the basis of cybernetic concepts, an integrated philosophical system, or "world view", proposing answers to the most fundamental questions about the world, ourselves, and our ultimate values.

Our methodology to build this complete philosophical system is based on a "bootstrapping" principle: the expression of the theory affects its content and meaning, and vice versa. In this way we aim to apply the principles of cybernetics to their own development. Our philosophy too is based on cybernetic principles. Our epistemology understands knowledge as a model, which is constructed by the subject or group, but undergoes selection by the environment. Our metaphysics asserts actions as ontological primitives. On the basis of this ontology, we define the most important concepts and organize them in a semantic network. At a higher level, we also lay out the fundamental principles of cybernetics in terms of these underlying concepts.

One of the central concepts is that of evolution in the most general sense, which is produced by the mechanism of variation and selection. Another is control, which we define in a special cybernetic sense, and assert as the basic mode of organization in complex systems. This brings us to the central concept for MSTT, that of the metasystem transition, or the process by which control emerges in evolutionary systems.

On this basis we then reconstruct the complete history of evolution, from the Big Bang to the present, as a sequence of MST's. An extrapolation of this sequence provides us with a first glimpse of what the future might bring. Finally, the possible dangers and opportunities of our evolutionary future direct our attention to the need for formulating an ethics, based on evolutionary and systemic principles, that could guide our actions.

## Background of the theory

The concept of the metasystem transition was introduced in Turchin's book The Phenomenon of Science, which was followed by Inertia of Fear and the Scientific Worldview. The basic tenets of MSTT were formulated by Turchin and Joslyn in "The Cybernetic Manifesto". As Heylighen joined the Editorial Board, the work on MSTT intensified, and the Principia Cybernetica Web was created. A major collection of papers on MSTT by the three editors and invited authors was published in a special issue of the journal World Futures entitled "The Quantum of Evolution". MSTT is also being applied to computer science and the foundations of mathematics by Turchin and his colleagues. The bibliography of PCP includes most publications on MSTT.

# Methodology for the Development of MSTT

Author: F. Heylighen, C. Joslyn,
Updated: Oct 6, 1997
Filename: METHODOL.html

The methodology used by the Principia Cybernetica Project to build a complete philosophical system is based on a "bootstrapping" principle: the form through which the knowledge is expressed affects its content, and vice versa. Thus, our theories about the evolutionary development of systems are applied to the development of the theory itself, while the structuring of concepts in the form of an evolving semantic network suggests new theoretical concepts (see form and content). A first requirement to develop such concepts is semantic analysis and consensus building about the meaning of terms. This meaning is as much as possible expressed formally through the links between nodes, resulting in a semantic network structure for the web.

Yet, we wish to avoid an over-formalization of the semantic structures we create. The meaning of a term will be partially formal, determined by the network of semantic relations to which it belongs; and partially informal, determined by the personal interpretation of the user who reads the exposition, and who tries to understand the concept by making associations with the context. Such a format allows the adequate representation of precise, mathematical concepts, of vague, ambiguous, "literary" development, and of the whole continuum in between. The degree of "formality" can be used to measure the position of a text on that continuum.

Vague or ambiguous concepts can be incrementally refined and clarified through the process of progressive formalization. Formalization may go in rounds, or levels, becoming more intensive and extensive. In keeping with this strategy, nodes we are writing will be initially organized according to the usual notion of their conceptual dependency understood informally or semi-formally. As the collection of nodes grows, we give more time to the work on formal semantics and the structuring of this accumulated material.

Both semantic networks and progressive formalization avoid starting from a fixed set of primitives or foundational concepts. Instead, we use part of the concepts to clarify other concepts, and vice versa. Thus, by allowing multiple beginnings to exist in parallel we avoid the shortcomings of foundationalism. The resulting system can be read or understood in different orders, for example starting from "meaning" as a primitive concept to develop the concept of evolution, or starting from evolution to analyse the evolution of meaning.

# Multiple Beginnings, Meta-Foundationalism

Author: C. Joslyn,
Updated: Aug 1993
Filename: MULTBEG.html

The emphasis that Principia Cybernetica places on consensus about fundamental concepts and principles can be criticized as risking the dangers of formalism and foundationalism, of adopting a deductive strategy in which cybernetic theory is linearly derived or proved from axioms. The risks of such approaches are obvious: either the development of an ossified, static philosophy which cannot adapt to new information; or an endless, futile search for the ultimate, jointly necessary and sufficient axiom set from which "truth" could be derived.

We are well aware of these dangers, but on the other hand we are also aware of the risk of the converse, of a failure to generate any firm foundations on which theory can be constructed. We believe that it is this latter condition that Cybernetics and Systems Science has indeed found itself in today. Even a cursory examination of current systems literature will reveal a veritable zoo of advanced, highly sophisticated theories which have only a loose and metaphorical relation to each other. A clear and elegant underlying theory on which they could be reconciled is simply lacking.

Rather the approach that we adopt aims to steer a middle ground between both extremes. It does so through the reliance on the general method we adopt throughout: a balance between the freedom of variation and the constraint of selection in a hierarchically organized system of control. In this case the multiple components of the hierarchy are foundations, axiomatic sets which reciprocally and irreducibly support each other. While each component is itself a stable foundation, the overall metasystem is a-foundational: the choice of an axiom set is ultimately either arbitrary or non-theoretical (pragmatic).

In this sense, the philosophy we propose is anti-foundational. Yet a constructive philosophy can be considered foundational in the sense that it takes the principle of constructive evolution itself as a foundation. This principle is different from other foundations, however, because it is empty (anything can be constructed, natural selection is a tautology), but also because it is situated at a higher, "meta" level of description. Indeed, constructivism allows us to interrelate and inter-transform different foundational organizations or systems, by showing how two different foundational schemes can be reconstructed from the same, more primitive organization.

# Multiple axiomatization sets, a metaphor for metafoundationalism

Author: C. Joslyn, F. Heylighen,
Updated: Sep 1993
Filename: MULTAXIO.html

A simple metaphor for our understanding of metafoundationalism can be found in mathematics. Mathematical systems are defined by a set of axioms, from which theorems are deduced. A mathematical theory might be defined as the set of all propositions that are true under the given set of axioms (theorems). For example, a theory of addition would contain propositions like "1 + 2 = 3", "2 + 5 = 3 + 4", and axioms like "a + b = b + a", "a + 0 = a", etc. Now, it is a well-known fact that in general the same theory can be generated by multiple sets of axioms. For example, the Boolean logic of propositions has many different axiomatizations which are formally equivalent (producing the same theorems), though one may be preferred to the other on the grounds of simplicity, esthetic appeal or explicitness.

Similarly, a metafoundational theory would consist of a set of propositions which can be derived from multiple sets of foundational propositions. Propositions which are primary (axioms) in one system would be derived in another system. No set of fundamental propositions would be absolutely primary. The whole theory should rather be viewed as a bootstrapping (cf. Heylighen's paper on "Knowledge structuring") network, where A derives from B and B derives from A.

A mathematical structure which might possibly express this arrangement is a multiply rooted DAG (Directed Acyclic Graph).

# Physical Constructivism

Author: F. Heylighen, C. Joslyn,
Updated: Jan 1992
Filename: PHYSCONS.html

The philosophy of the Principia Cybernetica Project also finds its basis in what we call "physical constructivism". While constructivism is traditionally known in its mathematical context, including the denial of reductio ad absurdum proofs, the existence of actually infinite objects, and the law of the excluded middle{As described more elsewhere , cite{TUV87a} is a constructive philosophy of mathematics from the perspective of Principia Cybernetica.} Cyberneticians especially have championed a broader interpretation that extends to psychology and the general philosophy of science.

Psychological constructivism asserts that knowledge is constructed by the subject, and not a simple "reflection" of or correspondence to reality. Following especially Kant, the neural mechanisms of the sense organs, the cortex, and the entire brain are seen as active mediators which provide the inherent "categories of perception". It follows that perception and knowledge are in fact a model of reality, and not merely a reflection or impression of it.

We can also describe an extreme version of radical constructivism, which is currently fashionable with some cyberneticians, but which we reject. Some radical constructivists approach strong skepticism by denying the existence of any external reality, and simply define reality as our knowledge. This "brain in a vat" view is unnecessarily strong. Instead we take a kind of agnostic view, which is a-realist, not anti-realist. While it is true that knowledge provides no direct and incorrigible access to the world, and it is not justified to make strong inferences about reality on the basis of knowledge, at the same time it is not allowed to make inferences about reality on the basis of a lack of knowledge: ignorance of something does not entail its non-existence.

We accept mathematical and psychological constructivism, but we go further. We call our evolutionary philosophy physically constructive in the sense that systems can only be understood in terms of the (physical) processes which manifest them and by which they have been assembled. This is certainly true for physical and biological systems, but also holds for formal, symbolic, and semantic systems. In particular, we hold that semantics, language, and mathematics must always be understood in the context of the physical basis of their operation---on the physical systems (e.g. sense organs, brains, machines, computers) which transmit, receive, and especially interpret physical tokens.

# Metaphysics

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Sep 10, 1997
Filename: METAPHYS.html

Philosophies traditionally start with a metaphysics: a theory of the essence of things, of the fundamental principles that organize the universe. Metaphysics is supposed to answer the question "What is the nature of reality?". But we cannot answer this question without first understanding what is the meaning of metaphysics, if any, and in what respect metaphysics differs from science, which tries to answer similar questions but through more concrete methods. Metaphysics is traditionally subdivided in ontology, the theory of being in itself, and cosmology, the theory describing the origin and structure of the universe.

In a traditional systems philosophy "organization" might be seen as the fundamental principle of being, rather than God, matter, or the laws of nature. However it still begs the question where this organization comes from. In our evolutionary-systemic philosophy, on the other hand, the essence is the process through which this organization is created. Therefore, our ontology starts from elementary actions, rather than from static objects, particles, energy or ideas. These actions are the primitive elements, the building blocks of our vision of reality, and therefore remain undefined. Actions are in not general not deterministic but involve an element of freedom. A sequence of actions constitutes a process. Our ontology is thus related to the process metaphysics of Whitehead and Teilhard de Chardin. Its historical origin can be traced back even further to the development from Kant to Schopenhauer.

Relatively stable "systems" are constructed by such processes through the mechanism of variation and selection. This leads to the spontaneous emergence of more complex organizations during evolution: from space-time and elementary particles, to atoms, molecules, crystals, DNA, cells, plants, animals, humans, and human society and culture (see the history of evolution). This developmental sequence provides us with a basis for our cosmology. Because of this self-organization of the universe, there is no need to posit a personal God, distinct from the universe, as an explanation for the observed complexity.

Events of emergence are the "quanta" of evolution. They lead to the creation of new systems with new identities, obeying different laws and possessing different properties. In such systems, the behaviour of the whole depends on the behaviour of the parts (a "reductionistic" view), but the behaviour of the parts is at the same time constrained or directed by the behaviour of the whole (a "holistic" view). (see downward causation)

A fundamental type of emergence is the "meta-system transition" , which results in a higher level of control while increasing the overall freedom and adaptivity of the system. Examples of metasystem transitions are the emergence of multicellular organisms, the emergence of the capacity of organisms to learn, and the emergence of human intelligence.

See further: Turchin's paper on Cybernetic Metaphysics.

# Metaphysics

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Sep 10, 1997
Filename: METAPHYS.html

Philosophies traditionally start with a metaphysics: a theory of the essence of things, of the fundamental principles that organize the universe. Metaphysics is supposed to answer the question "What is the nature of reality?". But we cannot answer this question without first understanding what is the meaning of metaphysics, if any, and in what respect metaphysics differs from science, which tries to answer similar questions but through more concrete methods. Metaphysics is traditionally subdivided in ontology, the theory of being in itself, and cosmology, the theory describing the origin and structure of the universe.

In a traditional systems philosophy "organization" might be seen as the fundamental principle of being, rather than God, matter, or the laws of nature. However it still begs the question where this organization comes from. In our evolutionary-systemic philosophy, on the other hand, the essence is the process through which this organization is created. Therefore, our ontology starts from elementary actions, rather than from static objects, particles, energy or ideas. These actions are the primitive elements, the building blocks of our vision of reality, and therefore remain undefined. Actions are in not general not deterministic but involve an element of freedom. A sequence of actions constitutes a process. Our ontology is thus related to the process metaphysics of Whitehead and Teilhard de Chardin. Its historical origin can be traced back even further to the development from Kant to Schopenhauer.

Relatively stable "systems" are constructed by such processes through the mechanism of variation and selection. This leads to the spontaneous emergence of more complex organizations during evolution: from space-time and elementary particles, to atoms, molecules, crystals, DNA, cells, plants, animals, humans, and human society and culture (see the history of evolution). This developmental sequence provides us with a basis for our cosmology. Because of this self-organization of the universe, there is no need to posit a personal God, distinct from the universe, as an explanation for the observed complexity.

Events of emergence are the "quanta" of evolution. They lead to the creation of new systems with new identities, obeying different laws and possessing different properties. In such systems, the behaviour of the whole depends on the behaviour of the parts (a "reductionistic" view), but the behaviour of the parts is at the same time constrained or directed by the behaviour of the whole (a "holistic" view). (see downward causation)

A fundamental type of emergence is the "meta-system transition" , which results in a higher level of control while increasing the overall freedom and adaptivity of the system. Examples of metasystem transitions are the emergence of multicellular organisms, the emergence of the capacity of organisms to learn, and the emergence of human intelligence.

See further: Turchin's paper on Cybernetic Metaphysics.

# The meaning of metaphysics

Author: V. Turchin,
Updated: Sep 1991
Filename: MEANMET.html

A metalanguage is still a language, and a metatheory a theory. Metamathematics is a branch of mathematics. Is metaphysics a branch of physics?

Meta' in Greek means over, and -- since when you jump over something you find yourself behind it in space and after in time -- it is also understood as behind and after. The word metaphysics' is said to originate from the mere fact that the corresponding part of Aristotle's work was positioned right after the part called physics'. But it is not unlikely that the term won a ready acceptance as denoting the whole field of knowledge because it conveyed the purpose of metaphysics, which is to reach beyond the nature (physics') as we perceive it, and to discover the true nature' of things, their ultimate essence and the reason for being. This is somewhat, but not much, different from the way we understand meta' in the 20-th century. A metatheory is a theory about another theory, which considered as an object of knowledge: how true it is, how it comes into being, how it is used, how it can be improved, etc. A metaphysician, in contrast, would understand his knowledge as a knowledge about the world, like that of a physicist (scientist, generally), and not as a knowledge about the scientific theories (which is the realm of epistemology).

If so, metaphysics should take as honorable a place in physics as metamathematics in mathematics. But this is very far from being the case. It would be more accurate to describe the situation as exactly opposite. Popularly (and primarily by the working masses' of physicists), metaphysics is considered as something opposite to physics, and utterly useless for it (if not for any reasonable purpose). I will argue below that this attitude is a hangover from the long outdated forms of empiricism and positivism. I will argue that metaphysics is physics.

A detractor of metaphysics would say that its propositions are mostly unverifiable, if intelligible at all, so it is hardly possible to assign any meaning to them. Thales taught that everything is water. The Pythagoreans taught that everything is number. Hegel taught that everything is a manifestation of the Absolute Spirit. And for Schopenhauer the world is will and representation. All this has nothing to do with science.

But Democritus, and then Epicurus and Lucretius taught that the world is an empty space with atoms moving around in it. In due time this concept gave birth to classical mechanics and physics, which is, unquestionably, science. At the time of its origin, however, it was as pure a metaphysics as it could be. The existence of atoms was no more verifiable than that of the Absolute Spirit. Physics started as metaphysics. This is far from an isolated case.

The question of verifiability is a part of our understanding of the nature of language and truth. What is the meaning of words and other objects of a language? The naive answer is: those things which the words denote. This is known as the reflection theory of language. Language, like a mirror, creates certain images, reflections of the things around us. With the reflection theory of language we come to what is known as the correspondence theory of truth: a proposition is true if the relations between the images of things correspond to the relations between the things themselves. Falsity is a wrong, distorted reflection. In particular, to create images which correspond to no real thing in the world is to be in error.

With this concept of meaning and truth, any expression of our language which cannot be immediately interpreted in terms of observable facts, is meaningless and misleading. This viewpoint in its extreme form, according to which all unobservables must be banned from science, was developed by the early nineteenth-century positivism (August Comte). Such a view, however, is unacceptable for science. Even force in Newton's mechanics becomes suspect in this philosophy, because we can neither see nor touch it; we only conclude that it exists by observing the movements of material bodies. Electromagnetic field has still less of reality. And the situation with the wave function in quantum mechanics is simply disastrous.

The history of the Western philosophy is, to a considerable extent, the history of a struggle against the reflection-correspondence theory. We now consider language as a material to create models of reality. Language is a system which works as a whole, and should be evaluated as a whole. The job the language does is organization of our experience, which includes, in particular, some verifiable predictions about future events an the results of our actions. For a language to be good at this job, it is not necessary that every specific part of it should be put in a direct and simple correspondence with the observable reality.

A proposition is true if, in the framework of the language to which it belongs, it does not lead to false predictions, but enhances our ability to produce true predictions. We usually distinguish between factual statements and theories. If the path from a proposition to verifiable predictions is short and uncontroversial, we call it a factual statement. A theory is but only through some intermediate steps, such as reasoning, computation, the use of other statements. Thus the path from a theory to predictions may not be unique and often becomes debatable. Between the extreme cases of statements that are clearly facts and those which are clearly theories there is a whole spectrum of intermediate cases.

The statement of the truth of a theory has essentially the same meaning as that of a simple factual statement: we assert that the predictions it produces will be true. There is no difference of principle: both factual statements and theories are varieties of models of reality which we use to produce predictions. A fact may turn out to be an illusion, or hallucination, or a fraud, or a misconception. On the other hand, a well-established theory can be taken for a fact. And we should accept critically both facts and theories, and re-examine them whenever necessary. The differences between facts and theories are only quantitative: the length of the path from the statement to verifiable predictions.

This approach has a double effect on the concept of existence. On the one hand, theoretical concepts, such as mechanical forces, electromagnetic and other fields, and wave functions, acquire the same existential status as the material things we see around us. On the other hand, quite simple and trustworthy concepts like a heavy mass moving along a trajectory, and even the material things themselves, the egg we eat at breakfast, become as unstable and hazy as theoretical concepts. For to-day's good theory is to-morrow's bad theory. We make and re-make our theories all the time. Should we do the same with the concept of an egg?

Certainly not at a breakfast. But in theoretical physics an egg is something different from what we can eat: a system of elementary particles. This makes no contradiction. Our language is a multilevel system. On the lower levels, which are close to our sensual perception, our notions are almost in one-to-one correspondence with some conspicuous elements of perception. In our theories we construct higher levels of language. The concepts of the higher levels do not replace those of the lower levels, as they should if the elements of the language reflected things "as they really are", but constitute a new linguistic reality, a superstructure over the lower levels. We cannot throw away the concepts of the lower levels even if we wished to, because then we would have no means to link theories to observable facts. Predictions produced by the higher levels are formulated in terms of the lower levels. It is a hierarchical system, where the top cannot exist without the bottom.

Recall the table describing four types of langage-dependent activities in our discussion of formalization. Philosophy is characterized by abstract informal thinking.

The combination of high-level abstract constructs used in philosophy with a low degree of formalization requires great effort by the intuition and makes philosophical language the most difficult type of the four. Philosophy borders with art when it uses artistic images to stimulate the intuition. It borders with theoretical science when it develops conceptual frameworks to be used in construction of formal scientific theories.

Top-level theories of science are not deduced from observable facts; they are constructed by a creative act, and their usefulness can be demonstrated only afterwards. Einstein wrote: "Physics is a developing logical system of thinking whose foundations cannot be obtained by extraction from past experience according to some inductive methods, but come only by free fantasy".

This "free fantasy" is the metaphysician's. When Thales said that all is water, he did not mean that quite literally; he surely was not that stupid. His water' should rather be translated as fluid', some abstract substance which can change its form and is infinitely divisible. The exact meaning of his teaching is then: it is possible to create a reasonable model of the world where such a fluid is the building material. Is not the theory of electromagnetism a refinement of this idea? As for the Pythagoreans, the translation of the statement 'everything is number' is that it is possible to have a numerical model of the Universe and everything in it. Is not the modern physics such a model?

When we understand language as a hierarchical model of reality, i.e. a device which produces predictions, and not as a true picture of the world, the claim made by metaphysics is read differently. To say that the real nature of the world is such and such means to propose the construction of a model of the world along such and such lines. Metaphysics creates a linguistic structure -- call it a logical structure, or a conceptual framework -- to serve as a basis for further refinements. Metaphysics is the beginning of physics; it provides fetuses for future theories. Even though a mature physical theory fastidiously distinguishes itself from metaphysics by formalizing its basic notions and introducing verifiable criteria, metaphysics in a very important sense is physics.

The meaning of metaphysics is in its potential. I can say that Hegel's Absolute Spirit is meaningless for me, because at the moment I do not see any way how an exact theory can be constructed on this basis. But I cannot say that it is meaningless, period. To say that, I would have to prove that nobody will ever be able to translate this concept into a valid scientific theory, and I, obviously, cannot do that.

It takes usually quite a time to translate metaphysics into an exact theory with verifiable predictions. Before this is done, metaphysics is, like any fetus, highly vulnerable. The task of the metaphysician is hard indeed: he creates his theory in advance of its confirmation. He works in the dark. He has to guess, to select, without having a criterion for selection. Successes on this path are veritable feats of human creativity.

# Knowledge and will

Author: C. Joslyn, V. Turchin,
Updated: Aug 1993
Filename: ^KNOWILL.html

[Node to be completed]

# From Kant to Schopenhauer

Author: V. Turchin,
Updated: Sep 29, 1997
Filename: KASCHO.html

It was noticed by the ancient Greeks already, that sensation is the main, and maybe the only, source of our knowledge. In the new time, Berkley and Hume stressed this in a very strong manner: things are our sensations. But rationalists still believed that some crucial ideas are inborn and have nothing to do with the imperfection of our sense organs.

Kant synthesized empiricism and rationalism by seeing knowledge as organization of sensations by our mind. Space, time, and other categories are not given us in sensations. They are our forms of perception, the way we organize sensations. This is how the synthetic judgments a priory become possible. They speak about the methods of our mind which are inborn and do not depend on sensations.

From the cybernetic point of view, sensations are at the input of our cognitive apparatus, the nervous system. This input is then processed by a huge hierarchical system. As the signals move up in the hierarchy, sensations become perceptions, and then conceptions (there are no sharp boundaries, of course). How much is coming from the reality, the sensations, and how much from the way we process them?

Kant considered the categories as a sort of final, ultimate, because they are rooted in the way our brains are made. The only possible geometry for him was Euclidean geometry.

And here comes the non-euclidean geometry of Lobachevsky. This could be a disaster if we did not interpret Kant's ideas from a modern point of view.

We see no contradiction between the use of inborn ways of analysis of sensation and the refusal to take these ways as the only possible and universally applicable. We cannot change our brain (for the time being, at least), but we can construct world models which are counter-intuitive to us.

We have two cybernetic systems which make world models: our brain, with its neuronal models, and our language, in which we create symbolic models of the world. The latter are certainly based on the former. But the question remains open: at what level of the neuronal hierarchy do the symbolic models take up?

Compare mathematics and classical mechanics. Mathematics deals with objects called symbolic expressions (like numbers, for example). They are simple linear structures. We use our nervous system to identify some symbols as "the same". For example, this symbol: A is the same as this: A. Another thing we want is to know that if you add a symbol B to A, and to another A you add another B, then the results, i.e. AB, will be identical again. The totality of such elementary facts could hardly be codified, exactly because of their basic nature. They are not eliminable. Even if we pick up a number of axioms about symbolic expressions, as we do, e.g., in the theory of semi-groups, we shall still use rules of inference to prove new facts about them, and since the rules and the formal proofs are again symbolic expressions, we shall rely again on the basic facts about symbolic expressions in the original informal way.

In classical mechanics we use much more of our neuronal world models. There is a three-dimensional space; there is time; there are the concepts of continuity, a material body, of cause and effect, and more.

Mach and Einstein would be, probably, impossible without Kant. They used the Kantian principle of separating elementary facts of sensations and organizing these facts into a conceptual scheme. But the physicists went further. Einstein moved from the intuitive space-time picture given by the classical mechanics down to the level of separate measurements, and reorganized the measurements into a different space, the four-dimensional space-time of the relativity theory. This space-time is now as counterintuitive as it was in 1905, even though we have accustomed to it. Hence what we call the paradoxes of the relativity theory. But they do not bother us. We use a bit less of neuronal models, and a bit more of symbolic models, that is all.

In quantum mechanics, the physicists went even further. They rejected the idea of a material body located in the space-time continuum. The space-time continuum is left as a mathematical construct, and this construct serves the purposes of relating micro and macro-phenomena, where it has the familiar classical interpretation. But material bodies lost their tangible character. The elementary objective facts are even lower in the hierarchy than measurements; they are observations which all occur in the world of macro-objects. In the relativity theory observations (measurements) at least belonged to the same universe as the basic conceptual scheme: the space-time continuum. In quantum mechanics, on the contrary, there is a gap between what we believe to really exist, i.e. quantum particles and fields, and what we take as the basic observable phenomena, which are all expressed in macroscopical concepts: space, time and causality.

Of course, one can say that in the last analysis every theory will explain and organize observable facts, and they always will be macroscopic facts, because we are macroscopic creatures. Thus a physical theory does not need the concept of real existence''; even if it is a micro-world theory it operates on macro-world observables. This is formally true. But the question is that of the structure of a physical theory. We still want our theory to give an answer to the question: what is REALLY existing? What is the ultimate reality of physics? This question is not meaningless. Its meaning is in the quest for a theory which would start with concepts believed to correspond to that ultimate reality, and then step by step construct observables from these really existing'' things. Somehow, it seems that such a theory has better chances for success. If we have such a theory, and the real existence is attributed to some things --- call them ex-why-zeds --- and the theory is born out by experiment, then we can say that the ex-why-zeds do really exist and that the world really consists of ex-why-zeds. Ontologically, this will be as certain as when we say that the apple is in a bowl on the basis of seeing it and touching it.

The contemporary quantum mechanics does not meet this requirement. It starts with space-time continuum, which in no sense exists. Since Kant we know that it is only a form of our perception.

Suppose we are determined to construct a theory which is built as required above. How should we go about the construction of such a theory? We must go further down in the hierarchy of neuronal concepts, and take them for a basis. Space and time must not be put in the basis of the theory. They must be constructed and explained in terms of really existing things.

This is where metaphysics should help us. The goal of metaphysics is to create world models which go down and down into the depth of our experience. The concepts of the higher level of the neuronal hierarchy are discounted as superficial; attempt is made to identify the most essential, pervasive, primordial elements of experience. But this is exactly the program we have just set for ourselves. Kant's metaphysics had served as the philosophical basis for the modern theories of physics. We see now that a further movement down is required. Thus let turn to the development of metaphysics after Kant.

Two lines of development became most visible: the German idealism and Hegel in particular; and Schopenhauer. The Hegelian line contributed to the development of the theory of evolution, but in terms of ontology and epistemology did not give much. It is not analytical. It is a romantic picture of a striving and struggling world. The basic entities and concepts are obviously made up, as if created by an artist.

Schopenhauer, on the contrary is analytical. He does not create a sophisticated picture of the world. He only gives an answer to the question what is the world': it is will and representation.

Kant introduced the concept of the thing-in-itself for that which will be left of a thing if we take away everything that we can learn about it through our sensations. Thus the thing-in- itself has only one property: to exist independently of the cognizant subject. This concept is essentially negative; Kant did not relate it to any kind or any part of human experience. This was done by Schopenhauer. To the question what is the thing-in- itself?' he gave a clear and precise answer: it is will. The more you think about this answer, the more it looks like a revelation. My will is something I know from within. It is part of my experience. Yet it is absolutely inaccessible to anybody except myself. Any external observer will know about myself whatever he can know through his sense organs. Even if he can read my thoughts and intentions -- literally, by deciphering brain signals -- he will not perceive my will. He can conclude about the existence of my will by analogy with his own. He can bend and crush my will through my body, he can kill it by killing me, but he cannot in any way perceive my will. And still my will exists. It is a thing-in- itself.

What then is the world as we know it? Schopenhauer answers: a 'Vorstellung'. This word was first translated into English as an idea', and then a representation'. Both translations are not very precise. In Russian there is a word for it which is a literal translation of the German Vorstellung': predstavleniye'. Vorstellung' is something that is put in front of you. It is a world picture we create ourselves -- and put in front of us, so that to some extent it screens the real world. This aspect of Vorstellung is not properly reflected either in 'idea' or in 'representation'.

Let us examine the way in which we come to know anything about the world. It starts with sensations. Sensations are not things. They do not have reality as things. Their reality is that of an event, an action. Sensation is an interaction between the subject and the object, a physical phenomenon. Then the signals resulting from that interaction start their long path through the nervous system and the brain. The brain is tremendously complex system, created for a very narrow goal: to survive, to sustain the life of the individual creature, and to reproduce the species. It is for this purpose and from this angle that the brain processes information from sense organs and forms its representation of the world. Experiments with high energy elementary particles were certainly not included into the goals for which the brain was created by evolution. Thus it should be no surprise that our space-time intuition is found to be a very poor conceptual frame for elementary particles.

We must take from our experience only the most fundamental aspects, in an expectation that all further organization of sensations may be radically changed. These most elementary aspects are: the will, the representation, and the action, which links the two: action is a manifestation of the will that changes representation.

Indeed, is it not the physical quantity of action that is quantized and cannot be less than Plank's constant h, if it is not zero? Why not see this as an indication that action should have a higher existential status than space, time, matter? Of course, it is not immediately clear whether the concept of action as we understand it intuitively and the physical quantity that has the dimension of energy by time and called 'action' are one and the same, or related at all. That the physicists use the word action' to denote this quantity could be a misleading coincidence. Yet the intuitive notion of an action as proportional to the energy spent (understood intuitively) and the time passed does not seem unreasonable. Furthermore, it is operators, i.e., actions in the space of states, that represent observable (real!) physical quantities in quantum mechanics, and not the space-time states themselves!

Even if we reject these parallels and intuition as unsafe, it still remains true that neither space, nor time, nor matter are characterized by constant indestructible quanta, but a combination of these: action. Is it not natural to take action as a basis for the picture of the world --- if not for a unifying physical theory?

But set aside physics. There is a branch of knowledge, cybernetics, where action ontology comes naturaly because of its approach to the description of the world. In cybernetics we abstract from matter, energy, space, even time. What remains is interdependence between actions of various kinds. Communication, control, information -- all these are actions. Taking action as an ontological primitive we come to an intuitively acceptable and logically consistent definition of its basic concepts.

# Action

Author: V. Turchin,
Updated: Oct 6, 1997
Filename: ACTION.html

Am Anfang war die Tat
(In the beginning there was the deed)

Goethe.
Schopenhauer's formula for all that exists is:
the world = will + representation
Will is manifested in action. Will and action are inseparable. We understand will as the quality that allows to choose between the (possible) options and act. Action and will are two faces of essentially the same phenomenon, and in our philosophy action is its perceivable part. We rewrite Schopenhauer's formula as follows:
the perceivable world = action + representation
Of these two parts, action has the ontological primacy. Representation is, in the last analysis, a kind of action -- interaction of the subject and the object of knowledge. Different subjects may have different representations of the same action. Only action as such has a definite reality: if it took place, it did. If did not, it did not.

Cybernetic ontology is ontology of action In cybernetics we abstract from matter, energy, space, even time. What remains is interdependence between actions of various kinds. Communication, control, information -- all these are actions.

An action is a result of a free choice. The state of the world defines (or rather is defined as) the set of feasible actions for each will. The act of will is to choose one of these. We learn about action through our representations, i.e. our knowledge about the external world.

When we ignore the agent, we speak of actions as events.

When we speak of actions of human beings we know very well what the agent is: just the person whose action it is. We reconstruct this notion, of course, starting from our own "I". When we speak of animals, e.g. such as dogs, we again have no doubt in the validity of the concept agent. This reasoning can be continued to frogs, amoebas, and inanimate objects. We say: "the bomb exploded and ship sank". But what about a collision of two elementary particles, of an act (sic!) of radioactive decay? It is definitely an action, but whose action is it? We do not know -- meaning that we have, at present, no picture, model, or theory of the world which would make use of the agent of this collision or emission. Thus we call this action an event. Not that it has no agent: by the nature of our concept, each action is performed by an agent. We simply can say nothing about it, so we ignore it. It may well be that in some future physical theory we shall speak about the agents of subatomic events. It seems reasonable to speak of an agent which comes into being for the express purpose of causing an act of radioactive decay. At each moment in time this agent makes a choice bewteen decay and not decay. This immediately explains the exponential law of radioactivity.

# Agent

Author: V. Turchin,
Updated: Oct 6, 1997
Filename: AGENT.html

An agent is a representation of an action. One action may not have more than one agent, but a single agent may cause more than one action. The necessity of this representation is seen when we describe a human being and want to distingush between the human's body and and the way (s)he acts. Generally, the concept of agent takes on the cybernetic description of some part of reality, and leaves physical (as well as chemical and bioligical) description to physics (chemistry, biology).

When we speak of an action, we speak also of an agent that performs the action. An agent is the carrier of will, the entity that chooses between possible actions. We do not see agents, we see only what they are doing. But we use the concept of agent to create models of the world. We break what is going on in the world into parts and call these parts actions. Then we notice that actions have certain structure. Some actions are taking place in parallel, others consecutively. A number of actions can be considered as one complex action (cf. process). We start the description of this structure by introducing the notion of agents that perform actions. The same agent may perform, consecutively, some number of actions. Different agents may execute actions in parallel. The agent of a complex action can, somehow, call a "subcontractor" agent.

Introduction of agents is, speaking informally, our first theory of the world. Further development of the theory can go in various directions. Since the thinking being understands agent seeing himself as the primary model, it is natural that in primitive societies the concept of agent is understood anthropomorphically: as something which is very similar, if not identical, to ourselves. Hence the animism of primitive thinking: understanding of all actions as initiated by various kinds of spirits or other imaginary creatures.

The development of modern science banned spirits from the picture of the world. But agents, cleared from anthropomorphism, still remain, even though the physicists do not call them so. What is Newtonian force if not an agent that changes, every moment, the momentum of a body? Physics concentrates on the description of the world in space and time; it leaves -- at least at the present day -- the concept of angent implicit. We need it explicitly because of our metaphysics based on the concept of action, not to mention the simple fact that cybernetics describes, among other things, the behavior of human agents. (This last field of application of cybernetics is, of course, one of the reasons for our metaphysics).

# Event

Author: V. Turchin,
Updated: Sep 1991
Filename: EVENT.html

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Event is an action abstracted from the agent.

# Emergence

Author: V. Turchin,
Updated: Sep 1991
Filename: EMERGE.html

[Node to be completed]

Agents come into, and go out of, existence. For centuries philosophers grappled with a problem: how to distinguish simple ("quantitative") changes from the cases where something really "new" emerges. What does it mean to be "new", to emerge? In our theory this intuitive notion is formalized as the coming of a new agent into existence. An action can lead to an emergence of new agents.

Take, once again, radioactive decay. A neutron suddenly chooses to break down into a proton, electron and neutrino. We saw one agent: the neutron. Now it disappeared, but we see three new agents which will meet their fate independently. This is an emergence.

In the case of complex actions, such as the birth of a baby, we can argue about the exact time of the event, because we have more than one reference system in which to describe actions. As a member of society, the baby emerges at birth. As an object of embryology it emerges at the moment of egg fertilization.

# Domain

Author: V. Turchin,
Updated: Sep 1991
Filename: DOMAIN.html

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A set of actions is referred to as a domain. Theories (models of the world) we construct are never universal. They are always applicable to some part of the reality only. This part is the domain of the theory. When we apply a theory, we assume that only those actions take place which are within the domain. Make an action which is not included in the domain, and the whole theory may become out of place. The states of the world are defined as subsets of the domain of the theory. Other actions are ignored; they may be either irrelevant, when they have no impact on the legitimacy of the theory, or prohibited, when they make the theory unapplicable.

# Distinction

Author: F. Heylighen,
Updated: Nov 25, 1997
Filename: DISTINCT.html

The simplest form or structure we can imagine is a distinction. A distinction can be defined as the process (or its result) of discriminating between a class of phenomena and the complement of that class (i.e. all the phenomena which do not fit into the class). As such, a distinction structures the universe of all experienced phenomena in two parts. Such a part which is distinguished from its complement or background can be called an indication (Spencer-Brown, 1969). If more than one distinction is applied the structure becomes more complex, and the number of potential indications increases, depending on the number of distinctions and the way they are connected.

A distinction can be seen as an element of cognitive structuration. Indeed, any process of perception implies a classification between phenomena. This classification operation has two aspects :

1. the phenomena which are put together in a class, are considered to be equivalent with respect to the observer's goals, they are assimilated, they belong to the same equivalence class ;
2. the phenomena corresponding to different classes are distinguished or discriminated, they belong to different equivalence classes.
The operations of distinction, and assimilation of phenomena necessarily go together. If a cognitive system would make no distinctions, only assimilations, it would be unable to perceive different phenomena, it would react to all situations in a uniform way ; hence, it would be unable to adapt to a changing environment. On the other hand, a system which would make no assimilations, only distinctions, would be unable to anticipate; hence it would also be unable to adapt.

Spencer-Brown (1969) has proposed general axioms for distinctions. With these axioms, he has shown that a set of distinctions has a Boolean algebra structure, isomorphic to the algebra of classes in set theory or to the algebra of propositions in logic (Spencer-Brown, 1969). Spencer Brown showed that distinction algebra implies propositional calculus. B. Banaschewski (1977) showed the opposite entailment in

See further:

# Freedom

Author: V. Turchin,
Updated: Sep 1991
Filename: FREEDOM.html

[Node to be completed]

In many minds, science is still associated with the deterministic picture of the world, as it was in the nineteenth century. The modern science, however, draws a picture which is quite different.

The world of the nineteenth century was, broadly, as follows. Very small particles of matter move about in virtually empty three-dimensional space. These particles act on one another with forces which are uniquely determined by their positioning and velocities.The forces of interaction, in their turn, uniquely determine, in accordance with Newton's laws, the subsequent movement of particles. Thus each subsequent state of the world is determined, in a unique way, by its preceding state.Determinism was an intrinsic feature of the scientific worldview of that time. In such a world there was no room for freedom: it was illusory. Humans, themselves merely aggregates of particles, had as much freedom as wound-up watch mechanisms.

In the twentieth century the scientific worldview has undergone a radical change. It has turned out that subatomic physics cannot be understood within the framework of the Naive Realism of the nineteenth century scientists. The theory of Relativity and, especially, Quantum Mechanics require that our worldview be based on Criti cal Philosophy, according to which all our theories and mental pictures of the world are only devices to organize and foresee our experience, and not the images of the world as it "really" is. Thus along with the twentieth-century's specific discove ries in the physics of the microworld, we must regard the inevi tability of critical philosophy as a scientific discovery -- one of the greatest of the twentieth century.

We now know that the notion that the world is "really" space in which small particles move along definite trajectories, is illusory: it is contradicted by experimental facts. We also know that determinism, i.e. the notion that in the last analysis all the events in the world must have specific causes, is illusory too. On the contrary, freedom, which was banned from the science of the nineteenth century as an illusion, became a part, if not the essence, of reality. The mechanistic worldview saw the laws of nature as something that uniquely prescribes how events should develop, with indeterminacy resulting only from our lack of knowledge; contemporary science regards the laws of nature as only restrictions imposed on a basically non-deterministic world. It is not an accident that the most general laws of nature are conservation laws, which do not prescribe how things must br, but only put certain restrictions on them.

There is genuine freedom in the world. When we observe it from the outside, it takes the form of quantum-mechanical unpredictability; when we observe it from within, we call it our free will. We know that the reason why our behaviour is unpredictable from the outside is that we have ultimate freedom of choice. This freedom is the very essence of our personalities, the treasure of our lives. It is given us as the first element of the world we come into.

Logically, the concept of free will is primary, impossible to derive or to explain from anything else. The concept of necessity, including the concept of a natural law, is a derivative: we call necessary, or predetermined, those things which cannot be changed at will.

# God

Author: Paul Harrison, F. Heylighen, V. Turchin,
Updated: Nov 5, 1997
Filename: GOD.html

 Synopsys: God: One or more hypothetical entities, normally invisible to humans, supposed to possess supernatural powers

The attributes of a god or God vary from one religion to another.

In polytheistic religion (poly = many, theos = god) several of these beings are posited. They are usually presumed to be immortal, and to control aspects of nature or human destiny. Although invisible, they are imagined to be human-like or animal-like in appearance.

In monotheistic religions (monos = one) God is usually viewed as an all- powerful and omnipresent being who created and still sustains the universe. He is thought to be incorporeal, but possessed of a human-like mind capable of planning actions, and of powers capable of carrying out those actions in the real world.

In Judaism, Christianity and Islam, God also has a human-like personal aspect, as the perfectly good, perfectly just, all-knowing judge of human actions and thoughts. He allegedly cares for and loves each one of us personally, and is merciful and forgiving if we accept him.

In the metaphysics of Principia Cybernetica, there is no need to posit the existence of a personal God, as an all-powerful, intelligent agent which governs the universe but which is external to it. Indeed, the role of God as creator and director of the universe is taken over by self-organizing evolution. On the other hand, if such an agent with the traditional attributes of omnipotence, omniscience and perfect goodness would be posited, this would lead to logical inconsistencies. There are many arguments supporting this conclusion.

However, this still leaves open a few philosophical positions. Which position you prefer is more a matter of taste than a matter of logic, since they seem equivalent in most practical respects. The simplest one is atheism, which assumes that there is no God, and thus no need to think about the concept. A more subtle approach is pantheism, where the word God is redefined and is equated with the universe and nature. In this spirit of pantheism, God might be seen as the highest level of control in the Universe. God is for the Universe what human will is for the human body. Natural laws are one of the manifestations of God's will. Another manifestation is the evolution of the Universe: the Evolution. Finally, there is the "intermediate" position of agnosticism, which simply assumes that we don't know whether God exists, since none of the existing arguments can prove that God either exists or does not exist.

# Arguments for and against the Existence of God

Author: Paul Harrison
Updated: Apr 3, 1997
Filename: GODEXIST.html

The polytheistic conceptions of God were criticized and derided by the monotheistic religions. Since the Enlightenment, monotheistic concepts have also come under criticism from atheism and pantheism.

## Arguments for the Existence of God

Philosophers have tried to provide rational proofs of God's existence that go beyond dogmatic assertion or appeal to ancient scripture. The major proofs, with their corresponding objections, are as follows:
1. Ontological:
It is possible to imagine a perfect being. Such a being could not be perfect unless its essence included existence. Therefore a perfect being must exist.
Objection: You cannot define or imagine a thing into existence.
2. Causal:
Everything must have a cause. It is impossible to continue backwards to infinity with causes, therefore there must have been a first cause which was not conditioned by any other cause. That cause must be God.
Objections: If you allow one thing to exist without cause, you contradict your own premise. And if you do, there is no reason why the universe should not be the one thing that exists or originates without cause.
3. Design:
Animals, plants and planets show clear signs of being designed for specific ends, therefore there must have been a designer.
Objection: The principles of self-organization and evolution provide complete explanations for apparent design.
3a. Modern design argument:
the Anthropic Cosmological Principle. This is the strongest card in the theist hand. The laws of the universe seem to have been framed in such a way that stars and planets will form and life can emerge. Many constants of nature appear to be very finely tuned for this, and the odds against this happening by chance are astronomical.
Objections: The odds against all possible universes are equally astronomical, yet one of them must be the actual universe. Moreover, if there are very many universes, then some of these will contain the possibility of life. Even if valid, the anthropic cosmological principle guarantees only that stars and planets and life will emerge - not intelligent life. In its weak form, the anthropic cosmological principle merely states that if we are here to observe the universe, it follows that the universe must have properties that permit intelligent life to emerge.
4. Experiential:
A very large number of people claim to have personal religious experiences of God.
Objections: We cannot assume that everything imagined in mental experiences (which include dreams, hallucinations etc) actually exists. Such experiences cannot be repeated, tested or publicly verified. Mystical and other personal experiences can be explained by other causes.
5. Pragmatic:
Human societies require ethics to survive. Ethics are more effectively enforced if people fear God and Hell and hope for Heaven (cf. the origin of ethical systems).
Objections: The usefulness of a belief does not prove its truth. In any case, many societies have thrived without these beliefs, while crime has thrived in theistic societies believing in heaven and hell.

### General objection against all the rational proofs for God:

Each of the above arguments is independent of the others and cannot logically be used to reinforce the others.
The cause argument - even if it were valid - would prove only a first cause. It would tell us nothing about the nature of that cause, nor whether the cause was mental or physical. It would not prove that the first cause was the personal, judging, forgiving God of Judaism, Christianity, or Islam. It would not prove the existence of a designer or of a perfect being. Equally, the design argument would prove only a designer, the ontological argument would prove only the existence of a perfect being, and so on. None of these arguments individually can prove that the cause, designer or perfect being were one and the same - they could be three different beings.

## Arguments against the existence of God

The major philosophical criticisms of God as viewed by Judaism, Christianity and Islam are as follows:

1. Evil:
Because evil exists, God cannot be all-powerful. all-knowing and loving and good at the same time.
2. Pain:
Because God allows pain, disease and natural disasters to exist, he cannot be all-powerful and also loving and good in the human sense of these words.
3. Injustice:
Destinies are not allocated on the basis of merit or equality. They are allocated either arbitrarily, or on the principle of "to him who has, shall be given, and from him who has not shall be taken even that which he has." It follows that God cannot be all-powerful and all-knowing and also just in the human sense of the word.
4. Multiplicity:
Since the Gods of various religions differ widely in their characteristics, only one of these religions, or none, can be right about God.
5. Simplicity:
Since God is invisible, and the universe is no different than if he did not exist, it is simpler to assume he does not exist (see Occam's Razor).

None of these criticisms apply to the God of pantheism, which is identical with the universe and nature.

# Pantheism

Author: Paul Harrison
Updated: Mar 28, 1997
Filename: PANTHEISM.html

 Synopsys: Pantheism is the philosophy that everything is God (pan="everything" theos="God") or that the universe and nature are divine

Pantheism is distinguished from panentheism, which holds that God is in everything, but also transcends the Universe.

Strict pantheism is not a theism. It does not believe in a transcendent or personal God who is the creator of the universe and the judge of humans. Many pantheists feel the word "God" is too loaded with these connotations and never use the word in their own practice - though they may use it to simplify, or to explain things to theists.

Pantheism has often been accused of atheism, and not just because it rejects the idea of a personal creator God. Strict or naturalistic pantheism believes that the Universe either originated itself out of nothing, or has existed forever. Modern scientific pantheism is materialistic. It believes that design in the universe can be fully accounted for by principles of evolution and self-organization. It does not believe in separate spirits or survival of the soul after death. Pantheists concerned about personal immortality seek it in realistic ways - through children, deeds, works, and the memories of the living.

Because it shares these naturalistic beliefs with atheism, the arguments for pantheism are the same as the arguments for atheism. Pantheism puts forward exactly the same critiques of transcendental religions and supernatural beliefs as does atheism. It is a secular religion, firmly rooted in the real world of the senses and of science.

This form of pantheism is identical with movements variously called religious atheism, affirmative atheism, Monism, or Cosmism. It is also very close to Taoism, some forms of Chinese and Japanese Buddhism, and neo- Confucianism.

Strict pantheism differs from conventional atheism only in its emotional and ethical response to the material universe. It focusses not simply on criticizing transcendental beliefs and religions, but stresses the positive aspects of life and nature - the profound aesthetic and emotional responses that most people feel towards nature and the night sky.

Naturalistic pantheism draws ethical conclusions from these feelings. Humans should seek a closer harmony with nature. We should preserve biodiversity and the delicate ecological balances of the planet, not just as a matter of survival, but as a matter of personal fulfilment.

Pantheism offers ways of expressing these feelings in ceremonies, celebrating significant times and places which underline our links with nature, the solar system and the universe. All this is possible without retreating one millimeter from the rigorously empirical attitude to reality found in modern science.

There are other forms of pantheism. Modern pagans frequently claim to be pantheists. Those who are concerned with logical consistency regard their various deities as symbolic rather than real. Those who are not so concerned combine pantheism with literal polytheism and belief in magic, reincarnation and other supernatural phenomena.

An alternative, quite common among New Agers, is pan-psychic pantheism - the belief that the universe/God has a collective soul, mind or will. This version was most clearly expressed by Hegel, and in more modern times by A. N. Whitehead and Teilhard de Chardin (see also: process metaphysics). Another variant is the idea that humans are in some way the mind of the universe (see also: the global brain). Our evolution - indeed our active help - is seen as helping the universe to attain its full potential (cf. Creative Immortality).

For further background, see:

# Atheism

Author: F. Heylighen,
Updated: Aug 8, 1994
Filename: ATHEISM.html

 Synopsys: Atheism is the philosophy that there are no gods ("a" = without, "theos" = god)

The simplest argument for atheism follows from Occam's Razor: from different equivalent explanations, choose the simplest one. If we cannot explain more things by postulating the existence of God than we can without, then we should prefer the theory without. The fact that this principle is not sufficient to prove that God does not exist, is not very relevant. After all, nobody can prove that unicorns, flying toasters or 23-legged purple elephants do not exist, but that does not make their existence any more likely. (see also: Occam's Razor as justification of atheism)

This assumes that the existence of God does not explain anything. However, the most typical argument for the existence of God is that creation by God is needed to explain the complexity of the universe around us. Apart from the fact that that same complexity can already be explained by straightforward principles of evolution and self-organization, the introduction of God does not in any way contribute to explanation, since it just pushes the phenomenon to be explained one step away. If God explains the existence of the universe, then what explains the existence of God? Since the concept of God is neither simpler nor more intuitive than the concept of the Universe, explaining God's origin is not in any way easier than explaining the origin of the Universe. On the contrary, since God is in principle unknowable, we cannot even hope to explain His coming into being. On the other hand, although the Universe may never be grasped in its totality, there are definitely many aspects of it that are observable and understandable, and lend themselves to ever more complete explanations. In conclusion, postulating God as an explanation does not only make the theory unnecessarily complicated, it even obscures those phenomena that might have been explained otherwise.

One must note that atheism is not in contradiction with religion. In its original, Latin sense, religion means "that which holds together", implying a kind of overarching philosophy and system of ethics that guides society as a whole, without necessary reference to God. Also in the more practical sense, several "religions", including Zen Buddhism, Taoism and Confucianism, lack any idea of God, and thus may be properly called "atheist religions". Also the different emotions that typically accompany religous experiences, such as the feeling of being part of a larger whole, can very well be experienced by a atheists, leading to what may be called "atheist religiosity".

# Basic Concepts of Science

Author: V. Turchin,
Updated: Sep 1991
Filename: SCICONC.html

[Node to be completed]

Cybernetics starts where metaphysics ends. It takes for granted the notions of system, process, state and control as the primary elements of models to construct. This is its difference from physics, which takes space, time and matter as the primary concepts. It does not follow that cybernetics may do completely without physical notions and vice cersa: the question is that of the main focus. Boundaries between various provinces of knowledge are blurred by constant lebding and borrowing.

# State of the world

Author: V. Turchin,
Updated: Sep 1991
Filename: STATE.html

[Node to be completed]

When we look at the modeling scheme, we see four nodes representing 'states of affairs', or 'states of the world'. What are those states? To answer this question let us ask ourselves: if action is the primary reality, how do we distinguish between various states of the world? The answer can be: by being able to do various different actions. For example, if the state of affairs is such that there is an apple on the table in front of me, a can reach it and pick it up. If there is no apple this is impossible. If the moon is on the night sky, I can execute the action of observing it. For this purpose I rotate my head in a certain way and keep my eyes open. Observation is a kind of action. Thus we could define a state of the world as a set of actions that I (the subject of knowledge) can take.

But there are states of another type, which do not fit this definition. If I feel pain, or am frustrated, or elated, angry, or complacent, this has no effect on the actions I can take. It affects only the choices I am going to make selecting from the same set of possible actions. Indeed, if my hand is over a gas heater and hurts (say, gently, for plausibility), I still have the choice between keeping the hand where it is, or withdrawing. But, obviously, the more it hurts, the more likley I am to withraw it.

Thus we come to distinguish between:

(a) a physical state, which is a set of possible actions for the subject 'physical' actions; and

(b) a mental state, which influences the choices to be made by the subject, but does not alter the set of possible choices.

When speaking of "states" without any of the two adjectives, we shall mean physical states.

The distinction between (a) and (b) reflects the fundamental distinction between "I" and "not-I".

Why should we consider action as more basic and primary than state? After all, we register an action when the states of the world changes. The reason is this: a state can be understood and characterized in terms of actions -- we have just defined it as a set of possible actions. An action, however cannot be defined through states. When we define an action as a change of the state, we introduce something new, which is not present in the idea of a state; change is, essentially, an action abstracted from the actor that executes it. The following observation confirms the primacy of action over state. When we start thinking about constructing a model of the world on the basis of these concepts, we tend to believe that we will need a relatively few types of actions, while the set of possible states of the world is expected to be much greater and much more complex.

In our mathematical model of semantics we shall denote the set of all possible action by A. We do not yet know what this set is, or rather what it should be for our further models to be successful. It is possible that various models of the world will start with various different sets A. But with any A, the set of possible states of the world, which we shall denote as W, is the powerset of A, W = P(A), i.e. the set of all subsets of A. Thus an individual state w is an element of W. w \el W, and a subset of A, w \subs A.

# Space

Author: V. Turchin,
Updated: Sep 1991
Filename: SPACE.html

[Node to be completed]

Among the most elementary actions known to us are small displacements "in space". We have put the quotes, because people have accustomed to imagine that some entity, called "space" exists as a primary reality, which creates the possibility of moving from one point of this space to another. Our analysis turns this notion topsy-turvy. Only actions constitute observable reality; space is nothing but a product of our imagination which we construct from small displacements, or shifts, of even smaller objects called points. If x is such a shift, then xx -- the action x repeated twice -- is a double shift, which we would call in our conventional wisdom a shift at the double distance in the same direction. On the other hand, we may want to represent a shift x as the result of another shift x' repeated twice: x = x'x'. It so happens that we can make three different kinds of shifts, call them x, y, z, none of which can be reduced to a combination of the other two. At the same time any shift w can be reduced to a properly chosen combination of shifts x, y, z. So we say that our space has three dimensions.

# Time

Author: V. Turchin,
Updated: Sep 1991
Filename: TIME.html

[Node to be completed]

When we do nothing for a while we say that some "time" has passed. In terms of actions, doing nothing is a special type of action. If we denote it by t, then tt is an action of waiting for two times longer than with t. When we measure time, we take some repetative process, like one swing of a pendulum, for a model of other processes. We may say, for instance, that John needes 80 'pendulums' of time to smoke a cigarette. In terms of the homomorphism picture, the state when John is lighting his cigarette is w_1; the state when he extinguishes it is w_2; the language L is the pendulum, with some kind of counter of swings; the mapping M is

registration of the current value of the counter. The process M must be a real physical process, not just a mental association of some states of the counter with some states of cigaret smoking - the truth which has been dramatically demonstrated by Einstein's relativity theory.

We often say that all real processes take place in space and time. The meaning of such statements is that in addition to what really goes on, we imagine some reference actions of consecutive shifts ("in space") and waits ("in time") and esatblish relationships between these actions and actual objects and processes. Thus, in accordance with Kant's view, space and time are not observable realities, but our ways to organize experience.

Henri Bergson was first to notice and emphasize the difference between real time, in which we live and act, and the objectified time of history and physics. Imagine a pendulum which at each swing puts a mark on a moving tape. We have a historical record of "the time moving". This historic record is an object at every moment we look at it. We use it as a part of our model of reality. We shall refer to the marks on the tape as representing a model time. It is very much different from the real time.

Real time is such that two moments of it never coexist. In model time the moments coexists as different objects in some space. Thus Bergson calls model time a projection of real time on space. Bergson's real time is irrreversible. Model time, the time of Newton's mechanics, is reversable: we read historical records equally well from left to right and from right to left. The seemingly inconceivable feature of Feynman's diagrams, the movement in the direction opposite to time, is explained simply by the fact that the time of physical theories is model time, i.e. a spacial phenomenon. Real time shows up in probability theory and statistical physics. We are dealing there with real acts of choosing from a number of possibilities. Hence this time is irreversible. In mechanics, to every action there is an inverse action which brings back the original state. So, when we project time on space the projection has an additional property of reversibility. But the act of choosing has no inverse. If you drew ticket No.13, you drew it. You can return it to the pool, but the fact will still remain that it was No.13, and nothing else, that was drawn first and then returned. You can choose, but you cannot "unchoose".

# Historic record

Author: V. Turchin,
Updated: Sep 1991
Filename: HISTOREC.html

[Node to be completed]

A model can be represented by an object in such a manner that the potential user of the model would be able to create the original model if he has the representation. In particular, any piece of knowledge can be represented in this way. This is a case of objectification. A a metasystem transition takes place: a model, which is a process, "becomes" (not really -- it is only represented by) an object; now we can manipulate these objectified models, modify them and study them, thus creating models of (objectified) models.

Some of these objectified models are not intended for a direct use, that is to say, they must not be used for obtaining predictions. They are used only as inputs to other models, so that those other models could do ingenious predictions and serve as a basis for decision making. We call such objectified models historic records. The manner in which they are used can be usually described as updating. That is, the user takes a historic record, modifies it according to some principles - may be with the use of other models, and derives the model to be actually used for the generation of predictions.

# Objectification

Author: V. Turchin,
Updated: Sep 1991
Filename: OBJFIC.html

[Node to be completed]

Semant.10. Objectification

We often want to think and speak about a process as something definit, constant -- in other words, as an object. Then we objectify it, i.e. replace the process, in reality or in our imagination, by an object. Objectification is a kind of metasystem transition. Normally in a metasystem transition we create a new process which controls the old one(s). In objectification the new process controls not the old ones, but the objects representing these processes.

The most usual form of objectification is definition. In mathematics, for instance, we define algorithms as computational processes which we expect to be executed in a certain fixed manner. The definition of an algorithmic process is an object, usually a text in some formal language. The semantics, i.e. the meaning, of the language is provided by a machine which executes the process in accordance with its definition. The famous Turing machine is an example. The concept of a machine gives us a link between objects and associated processes. A machine is a process which is controlled by its user in a certain quite limited way: the user only sets up the machine, i.e. puts into it certain objects (typically those would be the definition of an algorithm and some initial data for it) and starts it. After that the process goes on autonomously.

# Causality

Author: V. Turchin,
Updated: Sep 1991
Filename: CAUSE.html

[Node to be completed]

Let us analyze the meaning of statements of the form: 'C is the cause of the effect E', or simply 'C causes E'. Here C is an action, and E a situation, i.e. a set of states of the world deined by an abstraction function. Sometimes we speak of the cause C as a situation too, but what we mean then is that this situation leads inescapably to a certian action which causes E. Other times, we speak of the effect E as an action, but this, again, for the same reason that this action is an immediate result of the situation caused by C. So, in the mainstream, the cuase is an action, and the effect a situation.

The first attempt to explain the meaning of causality is to say that whenever C takes place, E takes place also. But this is not a satisfactory explanation. We see that after a day a night always comes, but the day is not the cause of the night; it is rather the rotation of the Earth. A simple observation of the sequence (C,E) is not sufficient. C and E might be two effects of a common cause, or this could be just a coincidence. That causal relation is something more than sequence in time, was noted by many thinkers and is in agreement with our intuition.

In search of the true meaning of causality consider the case when the cause C is my own action. By my own we mean a reference to the subject of knowledge. To give meaning to a statement, we must relate it to generation of predictions; as a rule, we do this in the form of modeling scheme (see model). The statement 'C causes E' is translated as two predictions:

The first prediction:

                      True            True
---------------
^               ^
|               |
S  |               |  E
|               |
|       C       |
----------------
The second prediction:
True           False
---------------
^               ^
|               |
S  |               |  E
|               |
|  do nothing   |
----------------

In words: if a situation recognized by abstraction S holds, and I do action C, then a situation characterized by abstraction E takes place. But if in the same situation I do nothing, then E does not occur. This precisely matches our intuitive understanding of causality.

Now suppose that C is not my action, but that of some other agent. It may come as a surprize that this, seemingly innocuous, change radically changes the picture. Now the action C must be a part of the initial situation S. We shall represent this complex situation as S+obs(C), where obs(C) stands for the fact that we observe some consequences of the action C. We must remember that I can perceive an action directly only when it is my own action; all other actions I perceive through my sense organs as changes in the state of the world. I can use the concept of action to construct modesl of reality; in particular, an action of a fellow human I perceive by analogy with my own action, but this does not alterate the basic fact that all agents except myself are given me only through the manifestation of their actions. I am I, and all other agents are they.

Thus we can try to express the meaning of causality in this case as consisting of the following pair of predictions:

The first prediction:

                      True            True
---------------
^               ^
|               |
S+obs(C)  |               |  E
|               |
|  do nothing   |
----------------

The second prediction:
True           False
---------------
^               ^
|               |
S  |               |  E
|               |
|  do nothing   |
----------------

However, this is not a correct expression of causality. This is only an observation of time sequence, even though in two versions. It could be that there exists a real cause C' which first causes action C, and then situation E. For example, I can, on some days, have scrambled eggs for breakfast. On such days I break two eggs and fry the content in a frying-pan. An external observer will see that when I break eggs, I always fry them; when I do not break eggs (say, because of having a cheese sandwich for breakfast), I do not fry them. This is not to say that the above two predictions cannot be part of our knowledge. They can. They constitute a useful model of my uninterrupted breakfest, which specifies the order of my actions. But it would be an error to conclude that breaking eggs is the cause of frying them.

Then is there any meaning in speaking about a cause which is not an action of the subject of knowledge? We believe, there is, but it is a result of a certain reduction of this case to the basic case where the cause C is the subject's action. There is an additional element introduced by the subject of knowledge in understanding causality in the case of sombody's else action. It is a tacit assumption that I could, somehow, allow or prevent the action of which I think as a cause. The interpretation of the statement that an action C of some agent causes E as the statement that if I, the subject of knowledge, allow C to take place, then E ensues; however, if I somehow prevent it, there will be no E. This case of causality is the following modesl of the world:

The first prediction:

                      True            True
---------------
^               ^
|               |
S  |               |  E
|               |
|    allow C    |
----------------
The second prediction:

True           False
---------------
^               ^
|               |
S  |               |  E
|               |
|   prevent C   |
----------------

This is how sombody's else action is reduced to mine. Allowing an action to happen is easy: just do nothing. But what about the feasibility of prevention? When I say that rotation of the Earth is the cause of alternation of day and night, the meaning of it is, according to our scheme, that if I stop the rotation of the Earth, the effect of alternation will disappear. But this clearly is impossible assumption. And it would not help to qualify this assumption as occuring in my imagination. Pictures in our imagination are constructed from pieces of our real experience. Stopping the Earth was not in our experience; it is unimaginable.

What I really imagine when I assume that I stop the rotation of the Earth is a relatively small (of the size convenient for imagination) rotating sphere, which I then cause tho stop rotation. This is a part of my model of the Earth and the Sun which explains the alternation of day and night. There are to models here. First I construct the Sun-Earth model, then I see that "days" and "nights" in that model are caused by the rotation of "the Earth", and then I make a jump back to real days amd nights and say that this is caused by the rotation of the Earth.

As required by our epistemology, we explained causal statements as certain models of the world. We might now ask: what about the concept of model itself? Does it not include the idea of cousality? The answer is yes, it does. Moreover it is based on causality. The ability to make predictions and the existence of causality in the world is, essentially, the same.

Of the four lines in the modeling scheme, one represents a free action of the subject of knowledge, the other three are functions, which are devices relying on causality: give me an inpout, and I will give output. To define formally what those functions are, we could describe them as causal relations, i.e. explain in terms of new models, metamodels. Then we could construct models of metamodels, etc. This we leave nowhere and add nothing to understanding. The concept of modeling is so fundamental that its meaning cannot be reduced to anyhting less than itself. Thus we take it for the basis.

We saw that the actions of the subject of knowledge make a necessary element in understanding causality. The relation of causality is not directly observable. We create models of the world using one or another machine to implement the causal, functional, relation. Then we apply our models to the world and see whether they work properly as predictors. Thus causality, as Kant first indicated, is not among our immediate sensual data, but is, rather, our basic way to organize those data.

# Object

Author: V. Turchin,
Updated: Sep 29, 1997
Filename: OBJECT.html

An object is a representation of a relation between actions. In the simplest case it is a subset of A1x A2, where A1 and A2 are some sets of actions. Such an object is a set of ordered pairs a1, a2> with ai \in Ai. Such a pair tells that if action a1 takes place, then it is followed by a2.

An egg can be defined by pairs of actions which include:

• (look at it and perceive a white spot)
• (throw it on the floorand perceive a small pool of liquid)
• (Keep it at the temperature of the mother-hen's body for a certain number of days and a chicken hatches), etc.
A more precise definition of an object can be achieved through relations including more variables, including those which stand for other representations: we allow here metasystem transitions which, in the last analysis take us back to actions.

Suppose I am aware of a tea-pot on the table in front of me. This is a result of my having the mechanism of abstraction in the brain. I recognize the image on my retina as belonging to a certain set of images, the abstraction tea-pot'.

But there is more to it. I perceive the tea-pot as an object. The object tea-pot' is certainly not a definite image on the retina of my eyes; not even a definite part of it. For when I turn my head, or walk around the table, this image changes all the time, but I still perceive the tea-pot as the same object. The tea-pot as an object must, rather, be associated with the transformation of the image on my retina which results from the changing position of my eyes. This is, of course, a purely visual concept. We can add to it a transformation which produces my tactile sensations given the position and movements of my fingers.

The general definition of an object suggested by this example consists of three parts.

1. First we define a set R{ob of representations which are said to represent the same object; in our example this set consists of all images of the tea-pot when I look at it from different view-points, and possibly, my sensations of touching and holding it.

2. Then from the set of all possible actions we separate a subset Acogn of actions which will be referred to as cognitive; in our case Acogn includes such actions as looking at the tea-pot, turning my head, going around the table, touching the tea-pot etc. -- all those actions which are associated with the registration of the fact that a tea-pot is there.

3. Finally, we define a family of functions fa(r), where for every cognitive action a \in A{cogn, the function

fa: R{ob \to R{ob

transforms a representation r \in R{ob into fa(r) = r' which is expected as a result of action a.

The most important part here is the third; the first two can be subsumed by it. We define an object b as a family of functions fa:

b = {fa: a \in Acogn}

It also can be seen as a subset of Acogn x Robx Rob.

The set Acogn is the domain of the index a; the set Rob is the domain and co-domain of the functions of the family.

When I perceive an object b, I have a representation r which belongs to the set Rob; I then execute some cognitive actions, and for each such action a I run my mental model, i.e. perform the transformation fa on r. If this anticipated representation fa(r) matches the actual representation r' after the action a:

fa(r) = r'

then my perception of the object b is confirmed; otherwise I may not be sure about what is going on. Observing a tea-pot I check my actual experience against what I anticipate as the result of the movements of my head and eyeballs. If the two match, I perceive the tea-pot as an object. If I travel in a desert and see on the horizon castles and minarets which disappear or turn topsy-turvy as I get closer, I say that this is a mirage, an illusion, and not a real object.

The concept of an object is naturally (one is tempted to say, inevitably) arises in the process of evolution. It is simply the first stage in the construction of the world's models. Indeed, since the sense organs of cybernetic animals are constantly moving in the environment, these actions are the first to be modeled. In the huge flow of sensations a line must be drawn between what is the result of the animal's own movements, and other changes which do not depend on the movements, are objective.

Description of the world in terms of objects factors out certain cognitive actions. Function fa factors out the action a by predicting what should be observed when the only change in the world is the subject's taking of the action a. If the prediction comes true, we interpret this as the same kind of stability as when nothing changes at all. The concept of object btings in a certain invariance, or stability, in the perception of a cybernetic system that actively explores its environment.

# Process

Author: V. Turchin,
Updated: Sep 1991
Filename: PROCESS.html

A process is a conceptual scheme (an abstraction of the second level). The concepts we call processes are characterized by the following features:

1. A process is an action which we see as a sequence of constituting sub-actions. The states of the world resulting from sub-actions are referred to as stages of the process. Thus we see a process as a sequence of its stages.

2. As is true for most concepts, the stages of processes are abstractions limited in space, i.e. they refer to certain spatially limited part of the world.

Very often processes have the following additional feature:

3. The process has definite initial and final stages. Furthermore, there is an abstraction from the initial stage called the input, and an abstraction from the final stage called output, of the process. For example, both stages may be some structures of objects, and some of these objects may constitute input while others constitute output. We then speak of the process as transforming the input into the output.

Also, there are processes which have a definite initial stage, but may or may not have a final stage, i.e. a process may be infinite. Processes often are contrasted with objects. The most important feature of objects is -- by definition -- their constancy with respect to certain cognitive actions. The concept of a process, on the contrary, represent an ongoing change.

The most usual way of speaking of objects and processes is a criss-cross between the two concepts: a process, the stages of which are objects not identical to each other. On a smaller time scale we observe constancy with respect to some cognitive actions, which gives us the reason to speek of it as an object. On a larger time scale, this is a process, and we say that the object is changing.

One can see from this definition that process is a very general concept. An object may be seen as a special kind of process where there is no change. Everything is a process, if we look at it in a certain way.

# Number

Author: V. Turchin,
Updated: Sep 1991
Filename: NUMBER.html

[Node to be completed]

The number is a conceptual scheme, an abstraction of the second level from specific numbers: 1, 2, 3, ... etc. The abstraction procedure to recognize specific numbers is counting.

Counting is based on the ability to divide the surrounding world up into distinct objects. This ability emerged quite far back in the course of evolution; the vertebrates appear to have it in the same degree as humans do. The use of specific numbers is a natural integrated description complementary to the differential description by recognizing distinct objects. This ability would certainly be advantageous for higher animals in the struggle for existence. And cybernetic apparatus for counting could be very simple -- incomparably simpler than for recognition of separate objects in pictures.

Yet nature, for some reason, did not give our brain this ability. The numbers we can directly recognize are ridiculously small, up to five or six at best (though it can be somewhat extended by training). Thus the number 2 is a neuronal concept, but 20 and 200 are not. We can use them only through counting, creating artificial representqations in the material external to the brain. The material may be, and was historically, fingers and toes, then pebbles, notches etc., and finally sophisticated signes on paper and electronic states of computer circuitry. For theoretical purposes the best is still the ancient-style representation where a chosen symbol, say '1' stands for one object. Thus 2 is '11', and 5 is '11111'.

# Infinity

Author: V. Turchin,
Updated: Sep 1991
Filename: INFINITY.html

[Node to be completed]

In mathematics and philosopy we find two concepts of infinity: potential infinity, which is the infinity of a process which never stops, and actual infinity which is supposed to be static and completed, so that it can be thought of as an object.

The cybernetic philosopy readily embraces the concept of potential infinity; in fact, it is hard to see how we can avoid it. We say that a process ends when it reaches a certain stage. In a particular case, we can define the end stage so that it never takes place. As every general notion, this is an abstraction: we have abstracted from the practical impossibilty to run any real process infinitely, and in fact, for very long. In this abstraction, no matter how long we have run the process, we always can do, or observe, the next step. This is why this infinity is called potential. At every specific stage the process involves no more than quite a finite reality; it is infinite only potentially.

For actual infinity we have no place in our system of concepts. On the intuitive level, we cannot imagine anything that would qualify as actual infinity, because neither we, nor our evolutionary predecessors never had anything like that in experience. When we try to imagine something infinite, e.g., infinite space, we actually imagine a process of moving form point to point without any end in sight. This is potential, not actual, infinity.

On a more formal level we can demonstrate the incompatibility of the concept of actual infinity with our cybernetic understanding of meaning. Indeed, suppose that some abstraction r represent the concept of an "infinite" object, and we use it while constructing a model. According to our semantics, there must exist an abstraction (representation) function F which recognizes whether a given state of the world belongs to this concept, an if so, results in r. Moreover, function F must, by definition, always require a finite time for coming up with a definite result. If the "infinite" object can be always recognized as such in a finite number of steps, it is not actually infinite, because it can be adequately replaced by a finite object. If, by the intuitive meaning of the "infinite" object r its recognition may require infinite time, then abstraction function will have to work, at least in some cases, infinitely, but then it is not a valid abstraction function. Thus we cannot use the concept of actual infinity at all.

As an example, consider the process of counting. We can imagine it going on infinitely if we do not count real sheep or apples, but simply produce concecutive numbers. Let us represent numbers (whole and positive) by strings of the symbol '1'; then the process is:

1, 11, 111, 1111, ... etc. infinitely.

When we say that this process is infinite, we mean that whatever is the current number, we can add one more '1' to it. Thus we deal with potential infinity.

To convert it into an actual infinity, we must imagine an object that includes in itself all whole numbers. We call it the set of all positive whole numbers. Suppose that such a thing exists. How would it be possible for an abstraction function F to distinguish it from other objects, e.g. from the set of all whole numbers with the exception of the number 10^{50}? Intuitively, F must examine the infinite number of the elements of the set. Since this is impossible to achieve in any finite time, the needed function F does not exist.

What we can do, however, is to create an objectification of the process which generates all whole numbers. A machine which initiates this process (and, of course, never stops) is such an objectification. This machine is a finite object. It can be made of metal, with an electric motor as the agent causing the process of work. Or we can describe it in some language addressing a human agent, but requirung only simple "mechanical" actions uniquely defined at each stage of the process. Such descriptions are known as {\algorithms}. If we use the English language for writing algorithms, the machine, to be referred to as N, could be as follows:

At the initial stage of the process produce the number '1'. At each next stage take the number produced at the preceding stage and produce the number obtained by adding '1' to it."

Now we can say that the set of whole numbers is N. We have no objections against sets defined in this way. Their meaning is crystal clear. Their infinity is still potential.

The problem with contemporary mathematics is that it operates with sets that cannot be represented by finite mechanical generators. They are uncountable.

The question of the nature and meaning of these sets is, in the eyes of contemporqry mathemticians and philosophers, wide open. Yet their usefulnes is abundantly demonstrated, and everybody believs that their use will never lead to contradiction. Thus it is important for us to interpret uncountable sets -- and the whole set theory -- in terms of our cybernetic, constructive philosophy. If we were unable to do this, it would undermine our claim that the basic principles on which we build our philosophy are universally applicable and sufficient.

Fortunately, we can interpret set theory, as well as classical and intuitionist logic, in our terms (see Foundations of logic and mathematics). Our interpretation assigns quite definite meanings, in our sense of the word, to the basic concepts of logic and set theory, and does it without any recourse to the concept of actual infinity.

If there were no way to interpret uncountable sets in our philosop

# Continuous vs. discrete

Author: V. Turchin,
Updated: Sep 1991
Filename: CONTIN.html

[Node to be completed]

A domain is continuous if for every action a there exist two actions a_1 and a_2 such that the result of the composite action a_1 a_2 is the same as the result of a.

An action a is elementary if a \not= a_1 a_2 for any two actions a_1 and a_2.

A domain is discrete if every action from it can be represented by a finite sequence of elementary actions.

# Observation

Author: V. Turchin,
Updated: Sep 1991
Filename: OBSERV.html

[Node to be completed]

Observation is an abstraction of knowledge from the impact made by mappings F(Wi) of the world's states Wi on the states Wi themselves. For instance, when we watch a paly of billiards, the positions of the balls are registered by means of the light thrown on the balls and reflected into our eyes. We rightfully believe that the effect of the lighting on the movements of the balls is negligible, so we speak about the play in a complete abstraction from the way we know about it. This separation is not always possible. Quantum mechanics deals with actions so elementary that the means we use to know of them cannot be abstracted from. Our ususal 'classic' notions of space and time include the abstraction of observation. Indeed, the mental construction of a reference frame uses shiftes and waits which are assumend to have no effect on the studied processes; for quantum-mechanical processes this assumption is not valid, and the classic space-time frame of reference looses its legitimacy, becomes meaningless. One should not use in mental constructions and experiments those things known not to exist.

# System

Author: C. Joslyn, V. Turchin,
Updated: Aug 1993
Filename: ^SYSTEM.html

[Node to be completed]

We now arrive at the concept of the system, which we obviously do not take to be fundamental. The definition of "system" is of course one of the great philosophical issues of Cybernetics and Systems Science. A common definition is "a group of units so combined as to form a whole and to operate in unison" \cite{WEB89}. But there are dozens of definitions in the technical literature, reflecting a wide range of philosophical perspectives:

• A technical definition similar to the common one, but not very specific, is offered by Hall and Fagan: "A system is a set of objects together with relationships between the objects \cite{HAAFAR56}".
• Beginning with Ashby \cite{ASR56}, systems theorists have been inclined towards antirealist definitions, avoiding ideas about entities with objective attributes. Instead they define systems in terms of the perceptions and distinctions drawn by people. In that direction, Rosen \cite{ROR86b} has emphasized that systems are cognitively constructed entities, created by people and only partially referring to "real" systems, or things. Systems are defined on things, and we should clearly distinguish between physical, "thinghood" and logical, "systemhood" properties. Brian Gaines reaches the solipsistic limit of this trend, defining a system as "what is distinguished as a system \cite{GAB79}".

• Formalists have been satisfied with a very simple and general definition: given a family of sets X_1, \ldots, X_n and their cartesian product X = \times_{i=1}^n X_i, then a system S is any relation in X: S \sub X \cite{MEM64b}.

• Constructivists decry the formalist strategy \cite{KAG89}, emphasizing that the natural world of evolving, emergent systems can never be captured by formal systems with fixed, finite, a prioiri universes of discourse. Instead they suggest open-ended systems which define their own elements and universes of discourse through the emergent processes of their own self-creation and self-modification.

Leaving aside issues of objectivity and subjectivity, realism and antirealism, and formalism and constructivism, there are some common characteristics which we can discover in all these definitions. System requires at least the following:

• A variety of distinct entities called elements.
• These elements are involved in some kind of relation.
• This relation is sufficient to generate a new, distinct entity, at a new, systemic level of analysis.

Thus in the concept of the system we see the unification of many sets of distinctions: the multiplicity of the elements and the singularity of the whole system; the dependence of the stability of the whole on the activity of the relation amongst the entities; the distinction between what is in the system and what is not; and finally the distinction between the whole system and everything else. In the activity of the relation which creates a stable whole, we recognize a closure. Therefore, we define a system as that distinction between the variety and stability of a closure on some multiple other distinctions. We call the stability of the whole the persistence of the system: as long as the multiple distinctions participate in the relation resulting in the stability of the whole system, then the system exists, and persists.

# Epistemology

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Jun 29, 1995
Filename: EPISTEM.html

What is knowledge? This is the basic question defining the domain of epistemology.

In MST Theory, knowledge is understood as consisting of models that allow the adaptation of a cybernetic system to its environment, by anticipation of possible perturbations. Models function as recursive generators of predictions about the world and the self. A model is necessarily simpler than the environment it represents, and this enables it to run faster than, i.e. anticipate, the processes in the environment. This allows the system to compensate perturbations before they have had the opportunity to damage the system.

Models are not static reflections of the environment, but dynamic constructions achieved through trial-and-error by the individual, the species and/or the society. What models represent is not the structure of the environment but its action, insofar as it has an influence on the system. They are both subjective, in the sense of being constructed by the subject for its own purposes, and objective, in the sense of being naturally selected by the environment: models which do not generate adequate predictions are likely to be later eliminated. Thus, the development of knowledge can best be understood as an evolutionary process characterized by variation mechanisms and selection criteria.

There is no "absolutely true" model of reality: there are many different models, any of which may be adequate for solving particular problems, but no model is capable to solve all problems. The most efficient way to choose or to construct a model which is adequate for the given problem is by reasoning on a metacognitive level, where a class of possible models can be analysed and compared. This requires a metasystem transition with respect to the variety of individual models.

# Knowledge

Author: V. Turchin,
Updated: Sep 1991
Filename: KNOW.html

[Node to be completed]

In cybernetics we say that a purposive cybernetic system S has some knowledge if the system S has a model of some part of reality as it is perceived by the system. Informally, a model of some process occurring in a system is a another system which somehow mimicks, or simulates, that process; thus by using the model it becomes possible to know something about the modelled process without actually running it, or predict developments before they actually happen.

The definition of the modeling scheme includes an important assumption of non-interference: that the procedure R (which is a sequence of actions), when it acts on a state w of the world, does not change that state. But we know from quantum mechanics that this assumption, strictly speaking, never holds. When we deal with macroscopic phenomena we can, nevertheless, use the non-interference assumpiton, because the impact of the instruments we use in R on the state w can always be made insignificant. But on the atomic and subatomic levels, we cannot abstract from the action of the means we use to get information about the objects studied.

There is one more non-interference assumption: the modeling procedure M_a imitating the action a does not alter in any way the course of the action, including the final result w_2. Usually, this assumption is easily true; the most salient case is that of an astronomer. His computations, certainly, do not change the motion of celestial bodies. But when we try to apply the modelling scheme to self-description, we may have problems.

So, a generalization of the modelling scheme is necessary, which would be applicable even though the assumption of non-interference may not be true. Consider these two two-staged processes which we see in the modeling scheme, and let them run in parallel so that the mutual influence is not excluded:

The first process:

1. R(w_1) results in r_1; 2. M_a(r_1) results in r_2.

The second process:

1. a results in w_2; 2. R(w_2) results in r_2'.

The prediction produced by the model is that r_2 = r_2'. We can go further, however, in the formalization of the concept of prediction and making it more universal. Wait for the time when both processes end and start the third process C which compares r_2 and r_2'. All three processes together constitute one complex process, which is nothing else but the process of testing the model. If our model is correct, then the process C, and with it the whole complex process that tests the model, will succeed, that is r_2 and r_2' will be found identical. The model predicts that its test will succeed. Therefore we introduce the formal concept of a prediction

However, even the definition of knowledge as a generator of predictions is not general enough to cover the whole concept. Pieces of our knowledge (statements, or propositions) may not necessarily produce verifiable predictions, but only some objects that will be used to produce prediction. Moreover, they may produce objects which produce objects which produce predictions, and so forth to any depth of the hierarchy of knowledge objectifications.

A simple example from mathematics: the equation x + y = y + x is not immediately verifiable, but it produces -- this is a shortcut for 'we know how to use it to produce' - such equations as 7 + 4 = 4 + 7. This object, in its turn, is still too abstract for a direct verification. We can, however, verify the prediction that four apples and seven apples can be added in either order with the same result: eleven apples.

We come, therefore, to the following definition: a piece of knowledge is an object which we can use to produce (generate) predictions or other pieces of knowledge. This definition is recursive; it allows a piece of knowledge which is not an immediate prediction, or prediction generator, to produce other similar pieces (including itself) any number of times, before some of its products will produce predictions. The possibility is not excluded that an object legitimately regarded as a piece of knowledge will infinitely produce other pieces without ever producing a single prediction: call this piece empty. This is because with our very general definition of recursive production (which we want to keep) we cannot always say in advance if a given piece will ever produce predictions. So we formally allow the existence of an empty piece of knowledge. Of course, if we can prove that a given object is an empty piece of knowledge we need not to regard it as knowledge at all.

A cybernetic system makes predictions in order to achieve certain goal, ultimately -- survival and proliferation.

True knowledge is an instrument of survival. Knowledge is power. There is no criterion of truth other than the prediction power it gives. Since powers, like multidimensional vectors, are hard to compare, there is no universal and absolute criterion of truth. Proclamation of any truth as absolute because of being given through a "revelation" is sheer self-deception.

# Model

Author: C. Joslyn, V. Turchin,
Updated: Aug 1993
Filename: MODEL.html

Metasystem Transition Theory understands knowledge as the existence in a cybernetic system of a model of some part of reality. The most immediate kind of a model is a metasystem which implements a homomorphic relation between states of two subsystems, a modeled system and a modeling system [ HeH94 RoR85a ].


_________________________________________|||
S|
|          _|_ _ _ _ _ _ _ _ |           |
|                         W              |
||         || w2 = L(w1)     ||          ||
|          | w1  ______- w2  |           |
||         _|_|_ _ _ _ _ _|_ |           ||
|             |           |              |
m1|= E(w1)    ||          || m2 = E(w2)  |
||         _|_|_ _ _ _ _ _|_ |           ||
|             |?          |?             |
|          | m1  ______-m2   |           |
||         || m2 = R(m1)     ||          ||
||         _|_ _ _ _ _ _ _M_ |           ||
|________________________________________|

Figure: The Modeling Relation

Formally, a model is a system S = <W, M, E> with:

• A modeled system or world W = <W, L> with states W = {wi} and actions or laws L: W -> W . For example, W could be the set of key presses of a computer operator or the physical world, while L is the behavior of the operator or natural law;

• A modeling system M = <M, R> with internal model states, or representations M = {mj} and a set of rules, or a modeling function R: M -> M. For example, M could be a set of symbol strings or neural signals, while the rules R are the activity of a computer or a brain;

• And finally a representation function E: W -> R. For example, E could be a measurement, a perception, or an observation.

When the functions L, R, and E commute, then we have m2 = R(m1) = R(E(w1)) = E(L(w1)) = E(w2). Under these conditions S is a good model, and the modeling system M can predict the behavior of the world W. We can call S a generator of predictions about W.

However, it is possible that M is itself a model, in which case S is a meta-model. The representation function then does not generate a prediction directly, but rather generates another model, which in turn can generate predictions. We come, therefore, to the understanding of knowledge as a hierarchical structure to recursively generate predictions about the world and the self, and which in turn allow the cybernetic system to make decisions about its actions.

# Homomorphism

Author: Turchin,
Updated: Sep 29, 1997
Filename: HOMOMORP.html

The mathematical definition of group homomorphism is as follows. Let G1 and G2 be groups with the operations O1 and O2, respectively. A mapping M from G1 to G2 is a homomorphism if M(O1(x,y)) = O2(M(x),M(y)) for all x,y \in G1.

The difference from modeling is that in homomorphism the operations are defined on the pairs of elements from the group, while in modeling the operations are defined on pairs where the first element is a state of the world or the model, and the second -- an action, in the world or the model, respectively.

# Prediction

Author: V. Turchin,
Updated: Sep 1991
Filename: PREDICT.html

[Node to be completed]

We define a prediction as a statement that a certain process, referred to as a test comes to a successful end, i.e. to a certain, specified in advance, stage, after which we simply do not care what happens to the process. The prediction that a test T is successful will be denoted as T!.

We define formally a generalized model as anything that produces one or more predictions. When we speak of producing predictions, we have in mind, of course, some objects that represent predictions, e.g. texts in a certain language which enable us to reproduce the process that the prediction is about. The objects representing processes are referred to as their objectifications (see).

Formally, we can fit our general concept of prediction into the frame of the modeling scheme, if we even further expand the range of possible actions a, namely, allow for a being an arbitrary process which may include both actions of the subject of the model, and any other actions and processes. Let the brain of the subject be always found in one of only two states, let them have the names True and False. The representation function M_a(w) will result in True if w is the end state of the process a which succeeded, and False otherwise. The modeling function M(r) will be universal and very simple: it immediately produces the object True. Now the model we built makes exactly one prediction : that the process a ends in success.

# Problem-solving

Author: F. Heylighen
Updated: May 19, 1998
Filename: PROBSOLV.html

 Synopsys: A problem is a situation which is experienced by an agent as different from the situation which the agent ideally would like to be in. A problem is solved by a sequence of actions that reduce the difference between the initial situation and the goal

A problem can be analysed into more specific components. First of all it consists of the two situations, the present one which we will call the initial state, and the desired one, which we can call the goal state. The agent's task is to get from the initial state to the goal state by means of series of actions that change the state. The problem is solved if such a series of actions has been found, and the goal has been reached. In general, problem-solving is a component of goal-directed action or control.

In simple control problems, the solution is trivial. For example, the thermostat is a control system or agent whose goal is to reach or maintain a specific temperature. The initial state is the present temperature. The action consists in either heating to increase the temperature or cooling to decrease it. The decision which of these two possible actions to apply is trivial: if the initial temperature is lower than the goal temperature, then heat; if it is higher, then cool; if it is the same, then do nothing. Such problems are solved by a deterministic algorithm: at every decision point there is only one correct choice. This choice is guaranteed to bring the agent to the desired solution.

The situations we usually call "problems" have a more complex structure. There is choice of possible actions, none of which is obviously the right one. The most general approach to tackle such processes is generate and test: apply an action to generate a new state, then test whether the state is the goal state; if it is not, then repeat the procedure. This principle is equivalent to trial-and-error, or to evolution's variation and selection. The repeated application of generate and test determines a search process, exploring different possibilities until the goal is found. Searches can be short or long depending on the complexity of the problem and the efficiency of the agent's problem-solving strategy or heuristic. Searches may in fact be infinitely long: even if a solution exists, there is no guarantee that the agent will find it in a finite time.

Such a search process requires a series of actions, carefully selected from a repertoire of available actions to bring the present state closer to the goal. Different actions will have different effects on the state. Some of these effects will bring the present state closer to the goal, others will rather push it farther away. To choose the best action at every moments of the problem-solving process, the agent needs some knowledge of the problem domain. This knowledge will have the general form of a production rule: if the present state has certain properties, then perform a certain action. Such heuristic knowledge requires that the problem states be distinguished by their properties. This leads us to a further analysis of problems.

Problem states will generally involve objects, which are the elements of the situation that are invariant under actions, and properties or predicates, which are the variable attributes of the objects. A problem state then can be formulated as a combination of propositions, where elementary propositions attribute a particular predicate to a particular object. The different values of the predicates determine a set of possible propositions, and thus of possible states. Since states that differ only in one value of one predicate can be said to be "closer" together than states that differ in several values, the state set gets the structure of a space, ordered by an elementary "distance" function. Actions can now be represented as operators or transformations, which map one element of the state space onto another, usually neighbouring, element.

The final component we need to decide between actions is a selection criterion, which tells the agent which of the several actions that can be applied to a given state is most likely to bring it closer to the goal. In the simplest case, an action consists simply of moving to one of the neighbouring states. Each state will then be associated with a certain value which designates the degree to which it satisfies the goal. This value may be called "fitness". The search space then turns into a fitness landscape, where every point in the space ("horizontal") is associated with a "vertical" fitness value, so that a landscape with valleys and peaks appears. Problem-solving then reduces to "hill-climbing": following the path through the fitness landscape that leads most directly upward.

The efficiency of problem-solving is strongly determined by the way the problem is analysed into separate components: objects, predicates, state space, operators, selection criteria. This is called the problem representation. Changing the problem representation, i.e. analysing the problem domain according to different dimensions or distinctions, is likely to make the problem much easier or much more difficult to solve. States which are near to each other in one representation may be far apart in another representation of the same problem. Therefore, a goal state that could be reached easily in one representation may be virtually impossible to find in another one. Problem representations are usually also models of the situation as experienced by the agent.

References:

.

# "Meta-Cognitive" Reasoning

Author: F. Heylighen, C. Joslyn,
Updated: Aug 1993
Filename: METACOGN.html

When we introduce concepts or models, their definition often requires more than the simple description of the world in terms of these concepts. It further requires an analysis of how we use a concept, how we happened to introduce it, how it could possibly be modified in order be more adequate, etc. Our reasoning in this process is metacognitive, a reasoning not about "the world as it is", but about the relation of our own knowledge to the world and to the goals we pursue.

The most efficient way to find or to construct a model which is adequate for the given problem is by reasoning on a metacognitive level, where a class of possible models can be analyzed and compared. This requires a metasystem transition with respect to the variety of individual models. Examples of metacognitive reasoning include the Principles of Variety Minimax and of incomplete knowledge.

# Principles of Reasoning with Uncertainty

Author: C. Joslyn,
Updated: Nov 27, 1995
Filename: MINIMAX.html

Cybernetics and Systems Science have produced some very important principles of information and uncertainty cite{CHR80a,KLG90b}. It should be emphasized that these principles were developed in the context of specific theories which represent information and uncertainty using a particular mathematical formalism of variety, usually probability and thereby stochastic information theory. Instead, we will here describe these principles using the general concept of variety.

These principles operate in the context of a metasystem S' = { S_i }, 1 < i < n. Typically the S_i are various models, descriptions, hypotheses, or other knowledge structures. The simplest measure of the variety of the metasystem S' is given by |S'| = n, the number of subsystems. Also, each S_i has its own variety V_i = V(S_i), e.g. a stochastic entropy.

The principles are:

## Uncertainty Maximization

In inductive reasoning, use all, but no more than, the available information.

The Principle of Uncertainty Maximization has a long and venerable history in inductive reasoning, including Laplace's Principle of Indifference or Principle of Insufficient Reason and Ockham's razor. It provides guidance in choosing a specific subsystem S_i from the metasystem S'.

It can be briefly summarized as follows: in selecting an hypothesis, use no more information than is available. More technically, given some data which constrains the hypotheses S_i such that each S_i is consistent with the data, then choose that hypothesis which has maximal uncertainty V_i.

The most successful application of the Principle is in stochastic systems, where it is the Principle of Maximum Entropy (PME). In combination with Bayesian statistics, the PME has a wide range of important applications as a general method of inductive inference in science, engineering, and philosophy of science cite{SKJ89b}. Like the Law or Requisite Variety, under certain conditions the PME is isomorphic to the 2nd Law of Thermodynamics cite{JAE57}.

## Uncertainty Minimization

In deductive reasoning, lose as little information as possible.

A corresponding Principle of Uncertainty Minimization is a more recent development cite{CHR80a}. It is less a principle of inductive inference or system selection than a principle of systems construction, how to construct a metasystem S' given some systems S_i.

It can be briefly stated as follows: in selecting a metasystem, use all information available. More technically, given some data and a class S = { S^j } of sets of hypotheses S^j = { S^j_i }, all of which are consistent with that data, then let the metasystem S' be that S^j such that V(S^j) is a minimum. Typically V(S^j) = |S^j|, but in a stochastic setting it is possible to consider V(S^j) as a higher order entropy.

Uncertainty Minimization provides a guideline in problem and system formalization. In particular, it relates to our views on meta-foundationalism. Given a situation where we have multiple consistent axiom sets S^j and a requirement to select one, then the Principle should be invoked.

## Uncertainty Invariance

When transforming a system, make the amount of information in the resulting system as close as possible to that in the original.

Like Uncertainty Minimization, this principle is also relatively new, developed by George Klir in 1991 cite{KlG93a}. Its use is to guide the scientist when transforming or translating a system or a problem formulation from one language or representational frame to another.

It can be briefly stated as follows: when doing such a transformation, aim to neither gain nor lose any information. This can, of course, be hard to do if uncertainty and information are represented in different ways in the different representational frames. Therefore, application of this principle is highly problem-specific. For example, uncertainty is measured most generally by the number of available choices, while in probability theory it is measured by entropy, and in possibility theory by nonspecificity. Therefore, when moving a problem formulation from one situation to another, the appropriate measures should be optimized to be as equal as possible.

More technically, given a system S and the metasystem S' = { S_i } of other systems with which we are attempting to model or represent S, the select that S_i such that V(S_i) is as close to V(S) as possible.

# The Principle of Incomplete Knowledge

Author: F. Heylighen,
Updated: Aug 1993
Filename: ^INCOMKNO.html

The model embodied in a control system is necessarily incomplete

This principle can be deduced from a lot of other, more specific principles: Heisenberg's uncertainty principle, implying that the information a control system can get is necessarily incomplete; the relativistic principle of the finiteness of the speed of light, implying that the moment information arrives, it is already obsolete to some extent; the principle of bounded rationality (Simon, 1957), stating that a decision-maker in a real-world situation will never have all information necessary for making an optimal decision; the principle of the partiality of self-reference (Lšfgren, 1990), a generalization of Gšdel's incompleteness theorem, implying that a system cannot represent itself completely, and hence cannot have complete knowledge of how its own actions may feed back into the perturbations. As a more general argument, one might note that models must be simpler than the phenomena they are supposed to model. Otherwise, variation and selection processes would take as much time in the model as in the real world, and no anticipation would be possible, precluding any control. Finally, models are constructed by blind variation processes, and, hence, cannot be expected to reach any form of complete representation of an infinitely complex environment.

# Evolutionary Approach to Epistemology

Author: F. Heylighen,
Updated: Jul 14, 1995
Filename: EVOLEPIST.html

Evolutionary epistemology is an approach that sees knowledge in the first place as a product of the variation and selection processes characterizing evolution. It notes, first, that the original function of knowledge is to make survival and reproduction of the organism that uses it more likely. Thus, organisms with better knowledge of their environments would have been preferred to organisms with less adequate knowledge. In that way, the phylogenetical evolution of knowledge depends on the degree to which its carrier survives natural selection through its environment.

Second, evolutionary epistemology notes that the individual, ontogenetic development of knowledge is also the result of variation and selection processes, but this time not of whole organisms, but of "ideas" or pieces of potential knowledge. Thus, the typical pattern of scientific discovery is the generation of hypotheses by various means (variation), and the weeding out of those hypotheses that turn out to be inadequate (selection).

This analogy between the creation of ideas and Darwinian evolution has been noted from the end of the 19th century on by different scientists and scholars (e.g. Poincaré and William James). It was first developed into a full epistemology of science by Popper, who spoke about "conjectures" and "refutations", and who noted that a fundamental criterion for every scientific theory is that it must be "falsifiable", i.e. able to undergo selection. The whole spectrum of evolutionary knowledge processes, from genetic mutation to scientific model-building, was first analysed by Donald T. Campbell, who also introduced the term "Evolutionary Epistemology".

Campbell's framework rests on three basic ideas:

1. the principle of blind-variation-and-selective-retention, which notes that at the lowest level, the processes that generate potential new knowledge are "blind", i.e. they do not have foresight or foreknowledge about what they will find; out of these blind trials, however, the bad ones will be eliminated while the good ones are retained;
2. the concept of a vicarious selector: once "fit" knowledge has been retained in memory, new trials do not need to be blind anymore, since now they will be selected internally by comparison with that knowledge, before they can undergo selection by the environment; thus, knowledge functions as a selector, vicariously anticipating the selection by the environment;
3. the organization of vicarious selectors as a "nested hierarchy": a retained selector itself can undergo variation and selection by another selector, at a higher hierarchical level. This allows the development of multilevel cognitive organization, leading to ever more intelligent and adaptive systems. The emergence of a higher-level vicarious selector can be seen as a metasystem transition.
To this basic framework, we must add a more detailed analysis of the different mechanisms of variation that create new ideas, of the way in which retained knowledge is stored and organized (knowledge representation), and of the different criteria that determine which knowledge will be selected and which one will be eliminated.

See also: Gary Cziko's and Donald T. Campbell's extensive of Selection Theory and Evolutionary Epistemology

# Vicarious Selectors

Author: F. Heylighen, & C. Joslyn,
Updated: Jul 14, 1995
Filename: VICARSEL.html

Knowledge is a mechanism that makes systems more efficient in surviving different circumstances, by short cutting the purely blind variation and selection they have to do.

A selector is a system capable of selecting variation. (Thus a selector can be understood as as agent of will. ) Knowledge functions as an anticipatory or vicarious selector . A vicarious selector carries selection out in anticipation of something else, e.g. the environment or "Nature" at large. For example, molecule configurations selectively retained by a crystal template are intrinsically stable, and would have been selected even without the presence of a template. The template accelerates, or catalyses, the selection, and thus can be said to anticipate, or to vicariously represent, the naturally selected configuration.

One can also imagine anticipatory selectors making different selections under different circumstances, compensating different perturbations by different actions. This kind of anticipatory selection has the advantage that inadequate internal variations will no longer lead to the destruction of the system, since they will be eliminated before the system as a whole becomes unstable. Thus anticipatory selectors select possible actions of the system in function of the system's goal (ultimately survival) and the situation of the environment. By eliminating dangerous or inadequate actions before they are executed, the vicarious selector forgoes the selection by the environment, and thus increases the chances for survival of the system.

An vicarious selector can be seen as the most basic form of an anticipatory control system or indeed of any model. A model is necessarily simpler than the environment it represents, and this enables it to run faster than, and thus anticipate, the processes in the environment. It is this anticipation of interactions between the system and its environment, with their possibly negative effects, that allows the system to compensate perturbations before they have had the opportunity to damage the system.

# Knowledge Selection Criteria

Author: F. Heylighen,
Updated: Sep 10, 1997
Filename: KNOWSELC.html

Whereas traditional epistemologies try to distinguish "true" knowledge from "false" knowledge by postulating one or a few unambiguous "justication" criteria (e.g. correspondence, coherence, consensus), in an evolutionary context we must admit that many different influences impinge on the evolution of knowledge. It is well-recognized from observations of concrete ecosystems and from computer simulations of complex evolutionary systems, that evolution is basically co-evolution of many different systems with complicated interactions, where system A tries to adapt to B, while B tries to adapt to A. This makes it very difficult to formulate fixed and objective criteria distinguishing "fit" systems from "unfit" ones. However, the definition of knowledge as a vicarious selector helping an organism to survive by anticipating perturbations, to some degree focuses the selection processes on the capacity of knowledge for prediction. Still, there are many different ways in which knowledge can support survival, and the predictive value can generally only be determined indirectly. This brings us to distinguish a number of different, mostly independent categories or dimensions, which each may contribute to the "fitness" of a piece of knowledge for its task, but which are not necessarily mutually consistent. The more a piece of knowledge fulfills each criterion separately, and the more criteria it fulfills in total, the better it is.

In cases where the different criteria contradict each other, no piece of knowledge can ever optimally fulfill all criteria. We rather need to look for a kind of "Pareto" optimality: different local optima, but such that we cannot combine the advantages of the different candidates into a global optimum that is best for all criteria. Whereas classical theories of knowledge would only recognize two categories of knowledge: true or false, more pragmatic epistemologies would rather order models from "more adequate" (precise, reliable) to "less adequate". In the present framework, this ordering is considered to be partial: it is not alway the case that one model is better than another one, and there is generally no best model. However, there are models which are better than other models and thus the theory avoids any "absolute relativism".

The different criteria can be categorized in three "super" classes:

1. objective criteria, which measure the reliability of predictions: distinctiveness, invariance and controllability
2. subjective criteria, which measure the ease with which individual subjects will accept new knowledge: individual utility, coherence, complexity, novelty
3. intersubjective criteria, which measure the fitness of the knowledge with respect to the community of carriers: formality, conformity, "infectiousness" or publicity, expressivity, collective utility, authority.
Thus, the criteria embody our "holistic" understanding of knowledge, which avoids reduction to either objective, subjective or social requirements, but acknowledges the roles that each of these realms plays in the spread of a successful piece of knowledge.

References:

.

# Distinctiveness

Author: F. Heylighen,
Updated: Sep 5, 1995
Filename: DISTINCTV.html

Knowledge consists of distinctions. A distinction is useful only insofar that it can be used to predict some deviation or variation from equilibrium. This entails that some change must follow or precede the appearance of the distinguished phenomenon; phenomena that do not make any difference, are not informative, and, hence, are not considered to be real in any practical sense. In other words, we need a "difference that makes a difference". For example, a sudden perturbation of the image on your retina because of dust in your eye will not be perceived as a distinct phenomenon. This requirement corresponds to Kelley's (1967) distinctiveness criterion for distinguishing "real" from "illusory" perceptions.

A related criterion for the reality of perceptions in Gestalt psychology is richness in contrast and detail: imagined or dream perceptions typically are fuzzy and contain little detail.

# Invariance

Author: F. Heylighen,
Updated: Sep 5, 1995
Filename: INVARIAN.html

The change following or preceding a distinguished phenomenon must not be unique, but share some properties with changes associated with similar phenomena. A minimal regularity or invariance of effect is needed in order to make predictions (Heylighen, 1989). The invariance criterion can be subdivided in a number of more specific criteria, specifying under which transformations of initial phenomena (causes) the characteristics of the effect will be invariant. The simplest type of invariance is probably that over time ("consistency" according to Kelley, 1967): subsequent appearances of the distinguished phenomenon should lead to similar effects. An effect can also be invariant over settings or circumstances, or over different points of view or modalities of perception. The more invariant the causal relationship, the more generally reliable and applicable it is, the more predictions can be made with it, and the more useful the corresponding piece of knowledge is.

If the same external phenomenon is perceived from different angles, distances or illuminations, it will appear differently, yet maintain some constant distinction or identity. The sensation produced e.g. by a a particle of dust in the eye, on the other hand, will not change when the person moves, or will change in a random way, without relation to the movement of the body.

According to atribution theory people attribute causes of perceived effects to those phenomena that covary with the effects, that is to say that are present when the effect is present, and absent when the effect is absent. To determine whether a perception is real, you should determine whether its cause is some objective, external phenomenon, or some internal mechanism (e.g. imagination, hallucination, malfunctioning of the perceptual apparatus).

Now, external or objective causes will not covary with changes that only affect internal or subjective variables. For example, some aspects of a perception of an external objects, such as size, and angle will covary with the movement of the perceiver (internal change). On the other hand, the fact that something is perceived at all, should not vary with the location of the observer: it should be invariant over positions. Kelley (1967) has proposed the following set of criteria that characterize external or "real" causes of perceptions:

a. invariance over time (consistency)
: a perception that appears or disappears suddenly is unlikely to be caused by a stable object

b. invariance over persons (consensus):
a perception on which different observers agree is more likely to be real than one that is only perceived by one person.

c. invariance over modalities:
this is an extension of the point above.
If the same phenomenon is perceived in different ways, or through different senses (e.g. sight and touch), it is more likely to objectively exist, rather than to be the effect of a malfunctioning perceptual system.

Remark that none of the above criteria is sufficient on its own to establish the reality of a perception. For example, a mass hallucination or a magician's illusion may be perceived by thousand people, and hence satisfy criterion b., without being caused by an objective phenomenon. A malfunctioning of the eye or of the nerves may be stable, and hence produce a continuing excitation of the brain, satisfying criterion a. A hologram can be looked at from different points of view (criterion c.), but does not correspond to a real object.

Yet, the perception will become more reliable or more real when the different criteria are fulfilled to a larger degree. The hologram cannot be touched (different modality), and that gives away its illusory character. The magician's spectacle may be watched by another magician who would quickly recognize the trick and expose the illusion. So, all other things being equal, the larger the number of people agreeing about the perception, the longer it lasts and the more modalities or points of view through which it is perceived, the more real it appears, and the more we can rely on it for making predictions. More generally, the more independent criteria it fulfills, the more real it will be.

# Coherence

Author: F. Heylighen
Updated: Mar 18, 1998
Filename: COHERENC.html

New ideas cannot just be assimilated as such: they must make sense with respect to what the subject already knows. Existing beliefs provide a "scaffolding" needed to support new ideas. This requirement for ideas to "fit in" the pre-existing cognitive system may be called coherence.

The problem remains to define what "coherence" precisely means: mere consistency is clearly not sufficient, since any collection of unrelated facts is logically consistent. In addition, coherence requires connection and mutual support of the different beliefs. Since learning is based on the strengthening of associations, ideas that do not connect to existing knowledge simply cannot be learnt. Connection means some kind of a semantic or associative relationship, so that information about one idea also gives you some information about the other. An idea can furthermore support another idea by providing an explanation, evidence or arguments why the latter idea should be true. The preference for consistency can be motivated by the theory of cognitive dissonance, which states that people tend to reject ideas that contradict what they already believe. More generally, it follows from the fact that a fit individual must be able to make clear-cut decisions. Mutually contradictory rules will create a situation of confusion or hesitation, which is likely to diminish the chances for survival.

Coherence as a criterion of truth is emphasized by the epistemology of constructivism, which is espoused by most cyberneticists, and emphasized in "second-order" cybernetics (von Foerster, 1996) and the theory of autopoiesis (Maturana & Varela, 1992). According to this philosophy, knowledge is not a passive mapping of outside objects (the reflection-correspondence view), but an active construction by the subject. That construction is not supposed to reflect an objective reality, but to help the subject adapt or "fit in" to the world which it subjectively experiences.

This means that the subject will try to build models which are coherent with the models which it already possesses, or which it receives through the senses or through communication with others. Since models are only compared with other models, the lack of access to exterior reality no longer constitutes an obstacle to further development. In such an epistemology, knowledge is not justified or "true" because of its correspondence with an outside reality, but because of its coherence with other pieces of knowledge (Rescher, 1973; Thagard, 1989).

References:

• Maturana H. R., & Varela F. J. (1992). The Tree of Knowledge: The Biological Roots of Understanding, (rev. ed.), (Shambhala, Boston).
• Rescher N. (1973) The Coherence Theory of Truth, London, Oxford University Press.
• Thagard, P. (1989) Explanatory Coherence. Behavioral and Brain Sciences 12: 435-467.
• von Foerster H. (1996) Cybernetics of Cybernetics (2nd edition). (Future Systems, Minneapolis)

# Utility

Author: F. Heylighen,
Updated: Sep 5, 1995
Filename: INDUTIL.html

A piece of knowledge will not only be selected for its objective reliability but for the degree to which it helps to solve personal problems. From two equally reliable models, one about the movement of stars in the Andromeda galaxy, and one about the movement of stocks at a stock exchange, the second one will obviously be much more successful, since it can be directly used to further one's own well-being by making money. Utility can be defined as the degree to which a piece of knowledge increases the competence of the user of that knowledge to reach his or her goals or solve his or her problems. Specific goals or values can be ultimately derived from the fundamental value of increased fitness for the actor. So, utility in an evolutionary context can be translated as "contribution to individual fitness".

# Conformity

Author: F. Heylighen,
Updated: Oct 3, 1997
Filename: CONFORM.html

 Synopsys: The more people already agree upon or share a particular idea, the more easily a newcomer will in turn be be converted to that idea, and the more difficult it will be for one already converted to reject that idea

This "conformity pressure" can be explained by the fact that the newcomer will be subjected to expressions of the idea more often, and will more likely get in trouble if he expresses dissonant idea (Heylighen, 1992). Though conformity pressure is mostly irrational, often rejecting knowledge that is adequate because it contradicts already established beliefs, its alternative formulation, "consensus", is a criterion of the invariance type, since it implies that a belief does not vary over individuals. More invariant rules are more reliable predictors, and consensus, in the sense where people agree about an idea because they independently came to the same conclusion, can be viewed as a quite rational criterion for selecting knowledge (cf. Kelley, 1967).

From another point of view, conformity is an expression of "meme selfishness" (Heylighen, 1992). As memory space is limited and cognitive dissonance tends to be avoided, it is difficult for inconsistent memes to have the same carriers. Cognitively dissonant memes are in a similar relation of competition as alleles: genes that compete for the same location in the genome. Memes that induce behavior in their carriers that tends to eliminate rival memes will be more fit, since they will have more resources for themselves. The concrete result is that a group of carriers with different memes will tend towards homogeneity, resulting from the imposition of the majority meme and elimination of all non-conforming memes.

Conformity is a necessary criterion for the evolution of cooperation, that is, for the selection of behaviors that are collectively useful, without being individually useful. As D. T. Campbell emphasizes, group selection often runs counter to the more powerful and direct force of individual selection. Therefore, he proposes a mechanism that suppresses individually selfish deviations from these collective beliefs: conformist transmission. As illustrated by the mathematical model of Boyd and Richerson (1985), all other things being equal, it seems evolutionarily optimal for subjects to adopt the majority or plurality belief rather than a minority idea. Thus, already popular ideas tend to become even more popular, leading to an eventual homogeneity of belief within a closely interacting group.

References:

# Collective Utility

Author: F. Heylighen,
Updated: Oct 3, 1997
Filename: COLLUTIL.html

The group equivalent of individual usefulness may be called collective utility. Some forms of knowledge benefit the collective, while being useless for an isolated individual. Languages, traffic regulations, technical standards and moral codes are examples of cognitive entities that have value only for intersubjective purposes. Such collective ideas will be selected at the group level: groups having such beliefs will be more fit than groups lacking them. This is how the supernatural cosmologies characterizing archaic civilisations discussed by Campbell (1997) have been selected.

# Authority

Author: F. Heylighen,
Updated: Oct 3, 1997
Filename: AUTHORIT.html

Complementary to the homogenizing influence of conformity, we find the diversifying effect of the division of labor. Because of their limited cognitive capacity, individuals within a complex society tend to specialize in a particular domain. As illustrated by (1994) computer simulation, this process of cognitive differentiation is driven by a positive feedback mechanism: individuals who were successful in solving a particular type of problem will get more of these problems delegated to them, and thus develop a growing expertise or authority in that domain. The backing of a recognized expert will contribute to the acceptance of a particular idea. This is the criterion of authority.

The integration on the level of norms and codes fostered by conformity and the differentiation on the level of expertise fostered by authority together produce a complexification of the social system. The process is similar to the metasystem transition which produced the differentiated organs and tissues in a multicellular organism. Thus, it leads to the further devlopment of a social superorganism.

# Epistemological Constructivism

Author: F. Heylighen,
Updated: Nov 12, 1997
Filename: CONSTRUC.html

The epistemology of (second order) cybernetics and of the Principia Cybernetica Project is constructivist. Ernst von Glasersfeld defines radical constructivism by the following two basic principles:

1. Knowledge is not passively received either through the senses or by way of communication, but is actively built up by the cognising subject.
2. The function of cognition is adaptive and serves the subject's organization of the experiential world, not the discovery of an objective ontological reality.
The importance of constructivism is best understood by comparing it with the opposite, more traditional, approach in epistemology or cognitive science, which sees knowledge as a passive reflection of the external, objective reality. This implies a process of "instruction": in order to get such an image of reality, the subject must somehow receive the information from the environment, i.e. it must be "instructed". The naive view is that our senses work like a camera that just projects an image of how the world "really" is onto our brain, and use that image as a kind of map, an encoding in a slightly different format of the objective structure "out there". Such a view runs quickly into a host of conceptual problems, mainly because it ignores the infinite complexity of the world. Moreover, detailed observation reveals that in all practical cases, cognition does not work like that. It rather turns out that the subject is actively generating plenty of potential models, and that the role of the outside world is merely limited to reinforcing some of these models while eliminating others (selection).

That construction serves in the first place selfish purposes: the subject wants to get control over what it perceives, in order to eliminate any deviations or perturbations from its own preferred goal state. Control requires a model of the thing to be controlled, but that model will only include those aspects relevant to the subject's goals and actions. In a sense, the subject does not care about the "thing" to be controlled, only about compensating the perturbations it senses from its goal, thus being able to adapt to changed circumstances.

## Background

Constructivism has its roots in Kant's synthesis of rationalism and empiricism (see Epistemology: introduction), where it is noted that the subject has no direct access to external reality, and can only develop knowledge by using fundamental in-built cognitive principles ("categories") to organize experience. One of the first psychologists to develop constructivism was Jean Piaget, who developed a theory ("genetic epistemology") of the different cognitive stages through which a child passes while building up a model of the world. In cybernetics, constructivism has been elaborated by Heinz Von Foerster, who noted that the nervous system cannot absolutely distinguish between a perception and a hallucination, since both are merely patterns of neural excitation. The implications of this neurophysiological view were further developed by Maturana and Varela, who see knowledge as a necessary component of the processes of autopoiesis ("self-production") characterizing living organisms.

Constructivist mechanisms are not limited to higher level learning or discovery of models, they pervade all evolutionary processes. The difference between Lamarckian and Darwinian evolutionary theory is just that Lamarck assumed that the environment somehow instructs an organism on how to be adapted. Darwin's view emphasized that an organism has to find out for itself, by trial and error. A similar conceptual transition from instruction to construction took place in the theories of immunity: the organism is not instructed in any way how to produce the right antibodies to stop the invaders, as was initially believed, it needs to generate all possible combinations by trial-and-error until it finds a type of antibody that works. Once such an antibody is discovered, the "knowledge" about how to fight that particular infection remains, and the organism becomes immune. The conceptual development from instructionism to selectionism or constructivism is well-described in Gary Cziko's book Without Miracles: Universal Selection Theory and the Second Darwinian Revolution.

Since constructivism rejects any direct verification of knowledge by comparing the constructed model with the outside world, its most important issue is how the subject can choose between different constructions to select the "right one". Without such a selection criterion, constructivism would lapse into absolute relativism: the assumption that any model is as adequate as any other. The two most often used criteria are coherence, agreement between the different cognitive patterns within an individual's brain, and consensus, agreement between the different cognitive patterns of different individuals. The latter position leads to "social constructivism", which sees knowledge solely as the product of social processes of communication and negotiation (the "social construction of reality"). We reject these positions are unduly restrictive, and take a much more pragmatic stance, where we note that the adequacy of knowledge depends on many different criteria, none of which has an absolute priority over the others. People can very well use incoherent models, over which there is no agreement with others, but which still are valuable for adaptation to a complex world. These criteria will include at least subjective coherence, intersubjective consensus, and (indirect) comparison with "objective" environment.

# Reflection-correspondence theory

Author: V. Turchin,
Updated: Sep 1991
Filename: REFCORR.html

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Compare our cybernetic epistemology with the classical reflection-correspondence theory of meaning and truth. One of the oldest questions of philosophy is: What is the meaning of words and phrases of a language? The naive answer is: those things which the words denote. This is known as the reflection theory of language. Language, like a mirror, creates certain images, reflections of the things around us. With the reflection theory of language we come to what is known as the correspondence theory of truth: a proposition is true if the relations between the images of things correspond to the relations between the things themselves. Falsity is a wrong, distorted reflection. In particular, to create images which correspond to no real thing in the world is to be in error.

With this concept of meaning and truth, any expression of our language which cannot be immediately interpreted in terms of observable facts, is meaningless and misleading. This viewpoint in its extreme form, according to which all unobservables must be banned from science, was developed by the early nineteenth-century positivism (Auguste Comte). Such a view, however, is unacceptable for science. Even force in Newton's mechanics becomes suspect in this philosophy, because we can neither see nor touch it; we only conclude that it exists by observing the movements of material bodies. Electromagnetic field has still less of reality. And the situation with the wave function in quantum mechanics is simply disastrous.

The history of the Western philosophy is, to a considerable extent, the history of a struggle against the reflection-correspondence theory. We now consider language as a material to create models of reality. Language is a system which works as a whole, and should be evaluated as a whole. The job the language does is organization of our experience, which includes, in particular, some verifiable predictions about future events an the results of our actions. For a language to be good at this job, it is not necessary that every specific part of it should be put in a direct and simple correspondence with the observable reality.

Unlike our dynamic concept of modeling as production of predictions, the classical concept of reflection is static. It immediately raises the questions like what does it actually mean that one thing "reflects" another. Also, how do we know that reflection takes place? To confuse things further, a distinction between mind and matter was made, which produced the question: how our ideas, belonging to mind, can reflect objects belonging to the realm of matter.

The cybernetic understanding of knowledge is much more precise. This precision is achieved by introducing dynamics into the picture. The mapping form the world to language present in the homomorphism picture is not required to be a "reflection"; we need not compare these two strata of reality. To see that the model works, we only have to compare things from the stratum of language.

All that has been said about language can be applied also to human thought. In cybernetic view, thought works because it implements some models of the world, not because it somehow statically reflects it. The difficult questions of the correspondence between the thought and its object simply do not arise.

In a static world no knowledge, no reflection or correspondence would be possible. Correspondence make sense only if we indicate a procedure which establishes what we want to call correspondence; and a procedure inescapably includes a time dimension.

# Subject of knowledge: "I"

Author: V. Turchin,
Updated: Sep 1991
Filename: SUBJ.html

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Knowledge is both objective and subjective because it results from the interaction of the subject (the cybernetic system) and the object (its environment). Knowledge about an object is always relative: it exists only as a part of a certain subject.

We can study the relation between knowledge and reality (is the knowledge true or false, first of all) if the subject of knowledge, a cybernetic system S , can become part of a metasystem S' in whose terms the knowledge about S and its relation to reality can be expressed.

The metasystem transition from S to S' generates a new type of knowledge. It is of the same nature as the metasystem transition from using a tool to making a new kind of tool, "metatools" , which serve to produce and improve tools. A

We can study the relation between knowledge and reality: first of all, whether a piece of knowledge is true or false. For that purpose the subject of knowledge, a cybernetic system S, must become part of a metasystem S' in whose terms the knowledge of S and its relation to reality can be expressed. But this only makes the knowledge relative to the system S'. This kind of metasystem transition may be repeated many times, resulting, possibly, in ever deepening knowledge, but at any time the knowledge will be still relative to a definite cybernetic system, for which it is a model used for obtaining predictions: nothing more, nothing less. The cybernetic concept of knowledge features an organic unity of the object and the subject of knowledge.

Some philosophers of the 19th century started reasoning with the distinction between "I" and "not-I"as the most primordial and self-evident fact of being. This sounded metaphysical and unnecessary to contemporary scientists, because they thought about the world as existing independently of my "I", and about their descriptions of the world's phenomena as completely objective. My "I" was simply part of the world, as well as your "I" and his "I".

The development of physics, however, demanded a careful attention to the process of obtaining our knowledge. The "I" crept into science as the observer of Einstein's relativity and the experimenter of quantum mechanics. We often speak of a reference system instead of the observer, and a measuring device instead of the experimenter, but this is a futile attempt to ban "I". A reference system and a measuring device are only ways to describe the activity of "I".

With the cybernetic notion of knowledge and meaning, "I" is firmly built into knowledge.

The following metaphor may help understand it. Knowledge is a tool. Each tool has a handle which you grasp when you use it. Different people (or even creatures from other planets) may use the same tool, but whenever it is used, somebody grasps the handle, and this is the "I" of knowledge, meaning and truth.

In the cybernetic foundation of mathematics [...] we show that if we start with the

principle that a meaningful mathematical proposition is a generator of predictions, we come to the necessity of introducing the user of the mathematical tools into the formalism. The tools of mathematics do not work autonomously, like Turing machines, but rather in an interactive mode. The user of such machines is, like Einstein's observer, a form of "I".

Semantic analysis discovers "I" in some of our most important notions. Take causality. What do we mean by saying that A causes B? We cannot observe this relation between phenomena. We can observe only a time sequence "B after A", but this may be two consequences of a common cause, or just a coincidence. The real meaning of the statement of causality includes my freedom to allow or not to allow A happen, to choose between A and not-A.

Then the meaning of "A causes B" is: if I allow A happen, B will take place too, but if I do not allow A, B will not take place. This is a meaningful proposition, a model of reality; but this model inextricably includes "I". To test causality, there must be a freedom to allow or not to allow A. "I" is the only source of this freedom. (See Causality for more detailed treatment.

The theory of probability relies heavily on "I". Recall the old paradox: we believe that events with probability zero (or vanishingly small) cannot happen. Meanwhile, every event that happens has the probability zero. The error here is thinking in terms of "objective reality". Probability is not an objective attribute of events. It is an instrument of decision taking, thus it becomes completely meaningless if there is nobody there to take decisions. Probability describes a relation between the subject of knowledge, "I", who now appears as a decision maker, and various events. Before an event we can speak of its probability, and it may be very small. After it took place, we cannot speak of its probability, because in decision taking we consider future, not past, events.

# Intuition

Author: V. Turchin,
Updated: Sep 1991
Filename: INTUIT.html

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We often use the words "intuition", "intuitively", meaning some act of our thought which cannot be decomposed into any constituting elements and, in particular, cannot be further explained or justified. This is the result of activating some non-verbal model of the world implemented in our brain. Sometimes we are very certain about what our intuition tells us; on other occasions we found ourselves in the state of doubt.

# Doubt

Author: V. Turchin,
Updated: Sep 1991
Filename: DOUBT.html

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Doubt is a result of having in the mind more than one models applicable to the current situation. Some of our brain models are built-in from birth, or develop in a short time after the birth. We can hypothesize that such models tend to be applicable in a unique way, so they do not invoke doubt. Other models develop in the process of life and are determined by our experience. It may well be that at different times two or more non-verbal models were developed which can be applied in the current situation. We perceive this as doubt. This meaning of the word 'doubt' is reflected in its etymology. 'Doubt' is, apparently descending from 'double', meaning a double opinion. In the Russian language the word for doubt is 'somnenie', which breaks down as 'so-mnenie', literally co-thought, a parallel thought.

# Language

Author: V. Turchin,
Updated: Oct 6, 1997
Filename: LANG.html

 Synopsys: A language is a system which, if properly controlled, can produce objects called messages

A language is a convention according to which certan material objects, to be referred to as linguistic objects, define certain actions, which are referred to as their meanings. There are two basic types of linguistic objects: commands and statements. Commands are used in the context of control, where the meaning of a command issued by the controlling system is the resulting action of the controled system. The meaning of a statement is the piece of knowledge (true or false), that is a hierarchical generator of predictions.

Human language is a multilevel system. On the lower levels, which are close to our sensual perception, our notions are almost in one-to-one correspondence with some conspicuous elements of perception. In our theories we construct higher levels of language. The concepts of the higher levels do not replace those of the lower levels, as they should if the elements of the language reflected things "as they really are", but constitute a new linguistic reality, a superstructure over the lower levels. Predictions produced by the higher levels are formulated in terms of the lower levels. It is a hierarchical system, where the top cannot exist without the bottom.

We loosely call the lower-level concepts of the linguistic pyramid concrete, and the higher-level abstract. This is a very imprecise terminology because abstraction alone is not sufficient to create high level concepts. Pure abstraction from specific qualities and properties of things leads ultimately to the lost of contents, to such concepts as something'. Abstractness of a concept in the language is actually its constructness', the height of its position in the hierarchy, the degree to which it needs intermediate linguistic objects to have meaning and be used. Thus in algebra, when we say that x is a variable, we abstract ourselves from its value, but the possible values themselves are numbers, which are not physical' objects but linguistic objects formed by abstraction present in the process of counting. This intermediate linguistic level of numbers must become reality before we use abstraction on the next level. Without it, i.e. by a direct abstraction from countable things, the concept of a variable could not come into being. In the next metasystem transition we deal with abstract algebras, like group theory, where abstraction is done over various operations. As before, it could not appear without the preceding metasystem level, which is now the school algebra.

There is another parameter to describe concepts of a language. This is the degree to which the language embedding the concept or concepts is formalized. A language is formal, or formalized, if the rules of manipulation of linguistic objects depend only on the form' of the objects, and not on their human meanings'. The form' here is simply the material carrier of the concept, i.e. a liguistic object. The human meaning' is the sum of associations it evokes in the human brain. While forms' are all open for examination and manipulation, i.e. are objective, human meanings'are subjective, and are communicated indirectly. Operations in formal languages can be delegated to mechanical devices, machines. A machine of that kind becomes an objective model of reality, independent from the human brain which created it. This makes it possible to construct hierarchies of formal languages, in which each level deals with a well-defined, objective reality of the previous levels. Exact sciences operate using such hierarchies, and mathematics makes them its object of study.

Classification of languages by these two parameters leads to the following four types of language-related activities :

 Concrete language Abstract language Unformalized language Art Philosophy Formalized language Descriptive sciences Theoretical sciences, mathematics

Art is characterized by unformalized and concrete language. Words and language elements of other types are important only as symbols which evoke definite complexes of mental images and emotions. Philosophy is characterized by abstract informal thinking. The combination of high-level abstract constructs used in philosophy with a low degree of formalization requires great effort by the intuition and makes philosophical language the most difficult type of the four. Philosophy borders with art when it uses artistic images to stimulate the intuition. It borders with theoretical science when it develops conceptual frameworks to be used in construction of formal scientific theories. The language of descriptive science must be concrete and precise; formalization of syntax by itself does not play a large part, but rather acts as a criterion of the precision of semantics.

# Message

Author: V. Turchin,
Updated: Sep 1991
Filename: MESSAGE.html

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A message is an object which has some meaning for some agent.

# Symbol

Author: V. Turchin,
Updated: Sep 1991
Filename: SYMBOL.html

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A symbol is the maximal unit (subsystem) of a language which can never be broken into parts. For instance, with natural human languages the morphemes (such as word roots, prefixes, prepositions etc.) play the role of symbols. In programming languages the symbols are usually referred to tokens.

# Sentence

Author: V. Turchin,
Updated: Sep 1991
Filename: SENTENCE.html

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A sentence is the minimal unit of a language which can have a meaning of its own, in separation from the rest of the message. Very often a message of a language is a sequence or a tree of sentences. Sentences of natural human languages are, with some reservations (e.g. concerning the use of pronouns) sentences in our formal sense. In programming languages sentences are usually called statements; this is in disagreement with the wide use of the term statement as denoting some description of a situation, like a proposition in logic. It is this wiser usage that we catch in our formalization of statement. Statements of programming languages are, in our terminolgy, commands (see).

# Parsing

Author: V. Turchin,
Updated: Sep 1991
Filename: PARSING.html

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Often the syntax of a language is defined by a non-deterministic procedure G which produces all legitimate meassages. Then the procedure which for any given object decides whether it could be produced by G, and if it could then by which steps of G, is known as parsing.

# Syntax

Author: Turchin,
Filename: SYNTAX.html

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# Semantics

Author: V. Turchin,
Updated: Sep 1991
Filename: SEMANT.html

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Our definition of knowledge allows us to further define meaning and truth. When we say or write something we, presumably, express our knowledge.

# Meaning

Author: V. Turchin,
Updated: Sep 1991
Filename: MEANING.html

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By the definition of a message, it must have a meaning. But what is meaning?

First of all, messages are always addressed to a certain agent. Also, they are supposed to influence the actions performed by this agent, otherwise there would no sense in producing and passing the message.

We know of exaclty two ways of changing the actions of an agent. One way is to change the state of the world as far as the agent is concerned. The other way is to change the agent's model of the world (if any). Thus by the type of their semantics messages of a language break into two categories: commands and statements.

# Command

Author: V. Turchin,
Updated: Sep 1991
Filename: COMMAND.html

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A command is a message which directly changes the state of the world as far as the receiver of the message is concerned, i.e. the set of possible actions for it. Changing the state of the world the sender of a command can prevent the receiver from doing something or open a possibility of doing something new. The meaning of a command is the way it changes the state of receiver. In the classical limiting case a command leaves the receiver no choice at all, so that the action of the receiver is uniquely determined by the command. Computer languages can serve as an illustration of this kind of semantics. Another example is control of a rocket from the earth. It involves a certain language of electromagnetic signals sent to the rocket. Here the command leaves almost no choice for the rocket, because there is no absolute precision of the implementation of continuous quantities. This situation is typical; what is meant by 'almost' is, of course varying from case to case.

# Statement

Author: V. Turchin,
Updated: Oct 6, 1997
Filename: STATEM.html

For primitive cybernetic systems which have no model of the world, the command language is the only language that can be received. If a system has a model of the world, as in the case of a human being or a robot, the message received may not restrict the actions of the system directly, but change the model of the wrld used by it. This will, obviously, influence its actions in an indirect way, and, possibly, not immediately. We call such messages statements. The content and the meaning of a statement is in the knowledge it passes to the receiver. It must be noted that when we simply say 'knowledge' we mean only its impact on the genration of predictions and do not distinguish between true knowledge and false (erroneous) knowledge.

Thus to be meaningful, a sentence must meet the same requirement as a piece of knowledge: we must know how to be able to produce predictions from it, or produce tools which will produce predictions, or produce tools to produce such tools, etc. If we can characterize the path from the statement to predictions in exact terms, the meaning of the statement is exact. If we visualize this path only vaguely, the meaning is vague. If we can see no path from a statement to predictions, this statement is meaningless.

We do not identify meaningless with worthless. Since our criterion of the meaningful includes a reference to the process of deriving predictions, the meaning is to some extent subjective, and what is meaningless for one person may be meaningful for another. Furthermore, seemingly meaningless statements may lead to -- through some mysterious processes in our brain -- new ideas and discoveries.

Our criterion of the meaningful should be used as a guide for making our ideas more clear and precise, not as a reason for categorical rejections.

# Instrumental meaning

Author: V. Turchin,
Updated: Sep 1991
Filename: INSTMEAN.html

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Our definition of meaning as potential knowldge assigns meaning only to messages, but not to their parts. Yet parts also have some meaning; we do not say that the word 'table' is meaningless. It has a meaning, but of its own kind, which we shall designate as instrumental, because it is used in assignin meaning to such sentences as 'This is John's table', or 'My table is is made of metal'. The preposition 'in' also has an instrumental meaning: it is used to construct such sentences as 'There is an apple in this box'. Usually it is clear from the context which kind of meaning we mean. One exception is our definition of a message as the minimal unit of the language that has a definite meaning. We must make it clear that we have in mind meaning proper, not instrumental meaning.

If we understand the instrumental meaning of a word, we can describe how precisely it is used in models. Since to describe models we have to describe processes which they involve, a description of instrumental meaning will, probably, also involve the description of cerain processes. For example, the meaning of such a noun as 'table' is in the abstraction process which recognizes an object as a table. This process is must be inevitably used in the meaning of any sentence including the word 'table'.

# Concept

Author: V. Turchin,
Updated: Sep 1991
Filename: CONCEPT.html

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A concept is any part of language which has an instrumental meaning. In human language we distinguish neuronal and logical concepts (see Human language).

The vertical lines in the modeling scheme are functions, or mappings R from the states of the world w_i to their representations r_i (the states of the brain of the subject system S). This mapping is always an abstraction: some aspects of the state of the world are necessarily ignored, abstracted from, in the jump from reality to its description. The role of abstraction is very important: it reduces the amount of information the system S has to process before decision making.

The term abstract concept or simply concept is often used, and will be used here, as asynonym of abstraction.

We often use the same word to denote both a process and its result. Thus all representations r_i resulting from mapping are also referred to as abstractions. We may say, for instance, that triangle is an abstraction from all specific triangles. It should be kept in mind, however, that abstraction, or an abstract concept, is not so much a specific representation (such as a language object), as the procedure which defines what is ignored and what is not ignored in the mapping. Obviously, the object chosen to carry the result of abstraction is more or less arbitrary; the essense of the concept is in the procedure that transforms w_i into r_i.

Given a representation function R and a specific representation r, we can construct a specialized function which will recognize the fact that a given state of the world w belongs to the abstraction r. We define this function as follows:

P_r(w) = True if R(w) = w,

False if R(w) =/ w.

Here True and False are two symbols chosen to represent two possible results of the function. Such functions as P_r are known as predicates (see). The predicate P_r(w) takes the value True for those and only those states w which are represented by r.

Whenever we speak of a predicate, we can also speak of a set, namely, the set of all states of the world w for which the predicate is true (takes the symbol True as its value). We shall call this set the scope of the abstract concept. Sometimes it is convenient to define an absraction through its scope, e.g., a member of John Smith's family can be defined by simple enumeration. But the universal way is the definition by property, in which case a reformulation through a set does not clarify the concept. If we "define" abstract triangle as the set of all specific triangles, it still remains to define what a specific triangle is. The real definition of an (abstract) triangle is a predicate, a procedure which can distinguish a triangle from everything else.

A cybernetic system may be interested, depending on its current purposes, in different parts, or aspects, of reality. Breaking the single all-inclusive state of the world w_i into parts and aspects is one of the jobs done by abstraction. Suppose I see a tea-pot on the table, and I want to grasp it. I can do this because I have in my head a model which allows me to control the movement of my hand as I reach the tea-pot. In this model, only the position and form of the tea-pot is taken into account, but not, say the form of the table, or the presence of other things, as long as they do not interefere with grasping the tea-pot. In another move I may wish to take a sugar-bowl. Also, I may be aware that there are exactly two things on the table: a tea-pot and a sugar-bowl. But this awareness is a result of my having two distinct abstractions: an isolated tea-pot and an isolated sugar-bowl.

The degree of abstraction can be measured by two parameters: its scope and level; correspondingly, there are two ways to increase abstraction.

The scope was defined above. The wider the scope of a concept, the more abstract it is. The classical example is: cats, predators, mammals, etc. up to animal. With this method, the content of the concept (i.e. the amount of specificity in its procedural definition) decreases as the degree of abstraction increases. We come finally to such universal abstractions as object or process which carry almost no information.

The level of an abstraction is the number of metasystem transitions (see) involved. In the modeling scheme the sibject-of-knowledge system S is a metasystem with respect of the world. Indeed, S {\controls} the world: it takes information from the world as input to create representation using R, processes it using M_a for various a, chooses a certain action a, and executes it at the output, changing thereby the state of the world. The brain of S, as the carrier of world representations, is on the metalevel, a 'metaworld', so to say. Assigning to the world level 0, we define the abstractions implemented in the brain as abstractions of the first level.

However, the creation of models of reality does not necessarily end with the first level abstractions. The brain of the level 1 can become the world for the new brain, the brain of the level 2. Naturally, the necessary machinery comes with it -- the second metasystem transition has taken place. Our human brain is known to have a many-level hierarchical structure. The "brain" of the first level is nothing but our sense organs. The eye contains the retina and the machinery necessary to through light on it and create representations of the world. The retina keeps first level abstractions and serves as the input for the next level of the brain hierarch whose representations will be abstractions of of the second level, etc.

The concept of number can serve as an example of an abstraction of the second level implemented in human language. Specific numbers are first-level abstractions. We derive them directly from our experience. The representation function for this model (arithmetic) is counting. The brain (in our technical sense) includes some physical carrier to keep specific numbers, such as an abacus, or paper and pencil. A specific number, say three is an abstraction from three apples, or three sheep, etc., whatever we are counting. The concept of a number is an abstraction from one, two , three, etc., which all are abstractions themselves which exist only in the first-level brain, and if not that brain, would not be found anywhere.

Another important example of a concept of the second level of abstraction is system (see).

Abstractions of the second and higher metasystem levels will also be called conceptual schemes.

In algebra the second-level abstraction number becomes a material for higher abstractions, and this metasystem transition can be repeated many times. When we say that the concepts used in mathematics or philosophy are very

abstract, we should realize that this high degree of abstraction comes not from the increase in scope, but increase in the level. We constract a language which is, like our brain, a multilevel hierarchical system. High level of abstraction goes in step with the high level of construction.

===============

!!! Abstraction breaks the world into parts by ignoring something but including something else. This refers to processes, not only to objects. In fact, the only possibility to define what is process (informally -- a part of the world) is to refer to an abstraction mechanism: what is left is a process, what is abstracted away is not this process, but "other processes"

\eon

\eon

Our language is a multilevel system. On the lower levels, which are close to our sensual perception, our notions are almost in one-to-one correspondence with some conspicuous elements of perception. In our theories we construct higher levels of language. The concepts of the higher levels do not replace those of the lower levels, as they should if the elements of the language reflected things "as they really are", but constitute a new linguistic reality, a superstructure over the lower levels. We cannot throw away the concepts of the lower levels even if we wished to, because then we would have no means to link theories to observable facts. Predictions produced by the higher levels are formulated in terms of the lower levels. It is a hierarchical system, where the top cannot exist without the bottom.

We loosely call the lower-level concepts of the linguistic pyramid concrete, and the higher-level abstract. This is a very imprecise terminology because abstraction alone is not sufficient to create high level concepts. Pure abstraction from specific qualities and properties of things leads ultimately to the lost of contents, to such concepts as something. Abstractness of a concept in the language is actually its constructness', the height of its position in the hierarchy, the degree to which it needs intermediate linguistic objects to have meaning and be used. Thus in algebra, when we say that x is a variable, was abstract ourselves from its value, but the possible values themselves are numbers, i.e. linguistic objects formed by abstraction in the process of counting. This intermediate linguistic level of numbers must become reality before we use abstraction on the next level. Without it, i.e. by a direct abstraction from countable things, the concept of a variable could not come into being.

There is another parameter to describe logical concepts. This is the degree to which the language embedding the concept or concepts is formalized. A language is formal, or formalized, if the rules of manipulation of linguistic objects depend only on the form' of the objects, and not on their meanings'. The form' here is simply the material carrier of the concept, i.e. a word, an expression. The meaning' is the sum of association it evokes in the human brain. While forms' are all open for examination and discrimination, i.e. objective, meanings' are subjective, and are communicated indirectly. Operations in formal languages can be delegated to mechanical devices, machines. A machine of that kind becomes an objective model of reality, independent from the human brain which created it. This makes it possible to construct hierarchies of formalized languages, in which each level deals with a well-defined, objective reality of the previous levels. Exact sciences operate using such hierarchies, and mathematics makes them its object of study.

Classification of languages by these two parameters leads to the following four types of language-related activities (I take the table from by book The Phenomenon of Science):

{\begtt

~ Concrete language Abstract language ~

~

Unformalized Art Philosophy language

~

~

Formalized Descriptive Theoretical language sciences sciences, ~ mathematics \endtt}

Art is characterized by unformalized and concrete language. Words and language elements of other types are important only as symbols which evoke definite complexes of mental images and emotions. Philosophy is characterized by abstract informal thinking. The combination of high-level abstract constructs used in philosophy with a low degree of formalization requires great effort by the intuition and makes philosophical language the most difficult type of the four. Philosophy borders with art when it uses artistic images to stimulate the intuition. It borders with theoretical science when it develops conceptual frameworks to be used in construction of formal scientific theories. The language of descriptive science must be concrete and precise; formalization of syntax by itself does not play a large part, but rather acts as a criterion of the precision of semantics.

\eon

\eon

A piece of knowledge is true if the predictions made by the subject (the user) of knowledge on the basis of this knowledge are true. A prediction that test T is successful (denoted as T!) is true if the test T is, in actual fact, successful.

What this definition says is, essentially, that for every statement S the statement 'S is true' is true if and only if S itself is true. We call such statements equivalent. But equivalency is not a tautology; the meanings of these two statements are different, even though they always are either both true or both false. To see this clearly we want to make more formal the system aspect of our definition.

Statements of a language represent some pieces of knowledge which can be reconstructed from statements by the recipient of the message. (which may be the sender of the message itself). These pieces of knowledge produce, ultimately, some predictions: T_1!, T_2!, ... etc. that certain tests are successful. Knowledge about the world constitutes a metasystem with respect to the world in which the tests T_1, T_2, ... etc. take place. When we speak of statements and determine their various properties, we create one more metasystem level; this is a metasystem transition. While the first level is the level of the use of knowledge, the second level is that of the analysis and evaluation of knowledge.

Generally, the construction of a new level of analysis and evaluation may be extremely fruitful and all-important. What we usually want are efficient procedures to decide whether statements are true or false. But as long as the semantics of truth statements goes, the results are more modest. The informal meaning of the statement 'S is true' is that some correct process of analysis of S concludes that S is true. Obviously, an attmept to define what is correct will lead us back to the concept of truth. Our definition avoids this loop by reducing 'S is true' to S. But if somebody insists on defining a correct process of evaluation, then the definition will be as follows. The evaluation process, when applied to a statement, is correct if it results in the value 'true' if and only if it results in 'true' when evaluating all predictions generated by this statement. When applied to a prediction T!, the correct evaluation process must result in 'true' if and only if the test T is successful. This definition is a reformulation of our original definition of truth.

The metasystem staircase does not, of course, end with the second level. We can create the third metasystem level to decide whether the statement 'S is true' is true, an so on infinitely. But if we only mean the semantics of it, then it is not worthwhile to do. Even though formally different, all these statements will be equivalent to each other.

We shall go along with the common understanding of the true and the false according to which what is not true is false, and vice versa. Now we put the question: What does it mean that a statement is false? To be meaningful, the statement of falsehood must be, as any other statement, interpreted as a prediction or a recursive generator of predictions. Let us start with the case of prediction.

Consider a prediction T!. If it is not true, there are two possibilities: that test T ends in failure, and that it never ends at all. The statement of the end in failure is an ordinary prediction: T runs, stops, we compare the result with Failure and this comparison succeeds. But what about the case when T never ends, i.e. is the statement that T is infinite?

The statement that a certain process is infinite is not a prediction. But it is a generator of predictions. Our definition of a prediction T! includes a procedure, let it be denoted as P(t_i), which is applied to the states t_i, i= 1,2,... etc. of the test T and determines, in a finite time, whether each given state is Success, Failure, or neither of the two, which we shall denote as Not-end. Therefore, the statement that the state t_i is Not-end is a prediction, namely, the success of the procedure that runs P(t_i) and compares the result with Not-end. Now we can define what is the meaning of the statement that T is infinite: it is a generator which produces the following row of predictions:

t_1 is Not-end

t_2 is Not-end

t_3 is Not-end

...

etc., infinitely

\eon

We easily agree that test is either finite or infinite. Now that we have the exact meaning of being infinite, we can formulate this assumption in its exact form.

We postulate and consider it self-evident that a test is either finite or infinite. This excludes any third, or middle, possibility, which gave this principle the name of the law of the excluded middle. From this we derive that a prediction may be only true or false. We further derive the law of the excluded middle for arbitrary statements. Consider some statement S which is a generator of predictions. Let us run this generator and define the following refutation process. Each time that a prediction if produced it it replaced by the symbol True if it is true and False if it is false. The first time the symbol False appears in the refutation process, the process ends and succeeds. This process is either finite, or infinite. If it is infinite, the statements S is true, otherwise it is false. No other outcome is possible.

\eon

Now that we know that a statement is either true or false, we want to classify them correspondingly. This is done through predicates.

We call predicate an agent which examines a statement and the world about which the statement is, i.e. the world of the tests T_1, T_2, ... etc., and comes with one of the two outputs: True if the statement is true, or False if false. The symbols True and False are referred to as truth values. Predicates are abstractions of the second level (see abstraction. In some cases we can construct predicates as machine procedures, in other cases a person or a group of persons may work for a long time in order to come with an answer.

In a wider sense, a predicate is any function which takes one of the two truth values. Such a function is understood as taking the input values, constructing a statement from them, and evaluating the truth value of this statement.

\eon

Up to know we have been speaking of truth in abstraction from the actual means we have to establish the truth of a statement. The most general term for establishing the truth of a statement is proof.

By proof of a statement S we mean any process which the subject of knowledge accepts as the evidence that S is true and is ready to use S for predictions on the basis of which to make decisions.

There are two cases of proof which can never arise any doubt because they do not base on any assumptions: verification and direct refutation.

Verification is a direct use of the definition of truth to decide if the statement is true.

Predictions are, in principle, verifiable. You only have to initiate the test process that the prediction is about and wait until it comes to the final state. Thus, if a prediction is correct, there is, in principle, a way to verify this, even though it may require such a huge time, that in practice it is impossible.

Now consider the situation when you want to verify a prediction T!, but the test process T is infinite. This means that the prediction is false, but you will never be able to verify this directly. To sum up, the following three cases are possible when we try to verify a prediction:

Testing process Prediction is

-------------------- ----------------- 1. ends in success verifyably true 2. ends in failure verifyably false 3. never ends false, but this is not directly verifyable

As to the verififcation of general statements (generators of predictions), it may be possible only if the number of prediction produced is finite. In the more usual case, with an infinite number of prediction produced, the direct verification is impossible.

\eon

A general statement which may produce an unlimited number of predictions can never by directly verified. But it may be directly refuted, i.e. found to b false. This happens when it produces a prediction T!, but in fact the test T ends in failure.

What we call theories are general statements which may produce infinite number of specific predictions. Theories are not directly verifiable, but they may be refuted.

\eon

When a statement cannot be directly verified or refuted, it is still possible that we take is as true relying on our intuition. For example, consider a test process T the stages of which can be represented by single symbols in some language. Let further the process T develop in such a manner that if the current stage is represented by the symbol A, then the next stage is also A, and the process comes to a successful end when the current stage is a different symbol, say B. This definition leaves us absolutely certain that T is infinite, even though this cannot be either verified, or refuted. In logic we call some most immediate of such statements axioms and use as a basis for establishing the truth of other statements. In natural sciences some of the self-evident truths serve as the very beginning of the construction of theories.

\eon

\eon

...

\eon

We distinguish between factual statements and theories. If the path from a statement to verifiable predictions is short and uncontroversial, we call it factual. A theory is a statement which can generate a wide scope of predictions, but only through some intermediate steps, such as reasoning, computation, the use of other statements. Thus the path from a theory to predictions may not be unique and often becomes debatable. Between the extreme cases of statements that are clearly facts and those which are clearly theories there is a whole spectrum of intermediate cases.

Top-level theories of science are not deduced from observable facts; they are constructed by a creative act, and their usefulness can be demonstrated only afterwards. Einstein wrote: "Physics is a developing logical system of thinking whose foundations cannot be obtained by extraction from past experience according to some inductive methods, but come only by free fantasy".

The statement of the truth of a theory has essentially the same meaning as that of a simple factual judgment: we refer to some experience which justifies, or will justify, the decision-making on the basis of this statement. When this experience is in the past we say that the truth is established. When it is expected in the future we say it is hypothetical. There is no difference of principle between factual statements and theories: both are varieties of models of reality which we use to make decisions. A fact may turn out to be an illusion, or hallucination, or a fraud, or a misconception. On the other side, a well-established theory can be taken for a fact. And we should accept critically both facts and theories, and re-examine them whenever necessary. The differences between facts and theories are only quantitative: the length of the path from the statement to the production of predictions.

\eon

\eon

# Abstraction

Author: V. Turchin,
Updated: Sep 29, 1997
Filename: ABSTRACT.html

There are two aspects of the concept of abstraction, as it is used: in the context of the modelling scheme *, and in the context of metasystem transition in the language.

The vertical lines on the scheme of modelling are functions, or mappings, from the states of the world W to representations R (the states of the language L of the model). This mapping is always an abstraction: some aspects of the state of the world are necessarily ignored, abstracted from, in the jump from reality to its description. The role of abstraction is very important: it reduces the amount of information the system S has to process before decision taking. The simplest case of abstraction, mathematically, is when the states of the world wi are described by a set of variables w1,w2,...,wn, and we can separate those variables, say w1,W2,...,wm, on which M really depends, from the remaining variables wm+1,...,wn, on which it does not really depend, so that

M(w1,w2,...,wm,x{m+1},...,wn) = M'(w1,w2,...,wm)

We often use the same word to denote both a process and its result. Thus all representations ri resulting from mapping will be also referred to as abstractions. It should be kept in mind, however, that abstraction is not so much a specific representation (a linguistc object), as the procedure M which defines what is ignored and what is not ignored in the mapping. Obviously, the object chosen to carry the result of abstraction is more or less arbitrary; the essense of the concept is in the transformation of wi into ri.

Mathematically, an abstraction ri can be defined as the set of all those states of the world w which are mapped on ri. i.e. the set of all w such that M(w) = ri. The abstraction 'tea-pot' is the set of all those states s in S which are classified as producing the image of a tea-pot on the retina of my eyes.

A cybernetic system, depending on its current purposes, may be interested in different parts, or aspects of reality. Breaking the single all-inclusive state of the world wi into parts and aspects is one of the jobs done by abstraction. Suppose I see a tea-pot on the table, and I want to grasp it. I can do this because I have in my head a model which allows me to control the movement of my hand as I reach the tea-pot. In this model, only the position and form of the tea-pot is taken into account, but not, say the form of the table, or the presence of other things on it. In another move I may wish to take a sugar-bowl. And there may be a situation where I am aware that there are exactly two things on the table: a tea-pot and a sugar-bowl. But this awareness is a result of my having two distinct abstractions: an isolated tea-pot and an isolated sugar-bowl.

The other aspect of abstraction results from the hierarchical nature of our language. We loosely call the lower-level concepts of the linguistic pyramid concrete, and the higher-level abstract. This is a very imprecise terminology because abstraction alone is not sufficient to create high level concepts. Pure abstraction from specific qualities and properties of things leads ultimately to the lost of contents, to such concepts as something'. Abstractness of a concept in the language is actually its constructness', the height of its position in the hierarchy, the degree to which it needs intermediate linguistic objects to have meaning and be used. Thus in algebra, when we say that x is a variable, we abstract ourselves from its value, but the possible values themselves are numbers, which are not physical' objects but linguistic objects formed by abstraction present in the process of counting. This intermediate linguistic level of numbers must become reality before we use abstraction on the next level. Without it, i.e. by a direct abstraction from countable things, the concept of a variable could not come into being. In the next metasystem transition we deal with abstract algebras, like group theory, where abstraction is done over various operations. As before, it could not appear without the preceding metasystem level, which is now the school algebra.

# Human language

Author: V. Turchin,
Updated: Sep 1991
Filename: HUMLANG.html

[Node to be completed]

# Neuronal vs. logical concepts

Author: V. Turchin,
Updated: Sep 1991
Filename: NEURLOG.html

[Node to be completed]

Two cybernetic systems are involved in human language and thought.

The first system is the human brain. It creates models of reality whose material body is the nerve nets, and which we therefore call neuronal models. Neuronal models use neuronal, or intuitive, concepts (functional elements).

The second system is language. Its functioning is linguistic activity in society. Linguistic activity creates models of reality whose material body consists of linguistic objects. The functional elements of this system are logical (linguistic concepts.

The two systems are tightly interrelated. Language is an offspring, and, in a certain sense, a continuation of the brain: using language we create new modesl of reality, which were not imbedded in our brain by nature. Some substructures in our brain are representations of the states of the world. Some of the linguistic objects are representations of those representations: we refer to them as most concrete, or low-level, concepts. Such words as "cat", "apple", "to run" , and the concepts they fixate, are of that kind.

But human language (like human brain) is a multilevel hierarchical system. We create theories, where we use abstractions of higher levels, logical concepts the machinery of which (do not forget that concepts are functional units) requires something in addition to the brain: some material linguistic objects. Thus while we can regard small numbers, like two or three, as neuronal concepts because we immediately recognize them, bigger numbers, like 137, can function only using some external to the brain representations.

The concepts of the higher levels do not replace those of the lower levels, as they should if the elements of the language reflected things "as they really are", but constitute a new linguistic reality, a superstructure over the lower levels. We cannot throw away the concepts of the lower levels even if we wished to, because then we would have no means to link theories to observable facts. Predictions produced by the higher levels are formulated in terms of the lower levels. It is a hierarchical system, where the top cannot exist without the bottom.

We loosely call the lower-level concepts of the linguistic pyramid concrete, and the higher-level abstract. This is correct as long as one keeps in mind that abstraction is not always abstraction in scope from things observable, but also, and most importantly, an abstractions from abstractions of the lower levels of the same linguistic system. Pure abstraction from specific qualities and properties of things leads ultimately, as its scope increases, to the loss of contents, to such concepts as some and something. Abstractness of a concept is actually its constructness', the height of its position in the hierarchy, the degree to which it needs intermediate linguistic objects to have meaning and be used. Thus in algebra, when we say that x is a variable, we abstract from its value, but the possible values themselves are numbers, i.e. linguistic objects formed by abstraction in the process of counting. This intermediate linguistic level of numbers must become reality before we use abstraction on the next level. Without it, i.e. by a direct abstraction from countable things, the concept of a variable could not come into being.

# Formalization

Author: V. Turchin,
Updated: Sep 1991
Filename: FORMALIZ.html

[Node to be completed]

When langage is used for comparatively narrow professsional purposes there is a tendency to limit the number of terms used and to give them more precise and constant meaning. We say that the language is being formalized. If this process is carried through to its logical conclusion, the language will be completely formalized, or formal. A language is formal if its usage relies only on the form' of linguistic objects, and not their intuitive meanings.

To make this definition precise, we must specify a set of perceptions (that is abstractions) and

actions which are registered and performed in the same way by all members of the society whom the languages serves. We shall refere to these perceptions and actions as universally defined. A language is formal if all the processes involved in its usage, namely the representation function R(w), the modelling function M(r), and (for command languages) the set of possible actions are expressed in terms of universally defined perceptions and actions.

We usually assume that universally defined perceptions and actions can be relegated to the machine. The question is still open whether this is a realistic assumption. We accept it with a qualification that if there is a doubt about a specific abstraction or action, it nust be excluded from the universally defined set. The a formal language is a language usable by a properly constructed machine. A machine of that kind becomes an objective model of reality, independent from the human brain which created it. This makes possible a series of consecutive metasystem transitions, where each next level deals with a well-defined, objective reality of the previous levels. Thus the language becomes an ultrametasystem, exhibiting the stair-case effect (see [Tur77]) and an explosive growth in volume and power. Just as mastering the general principle of using tools to make better tools gives rise to consecutive metasystem transitions and the creation of industrial system, so mastering the principle of describing (modelling) by means of a formalized language gives rise to the creation of the hierarchical system of formal languages on which the modern science is based.

# Four types of linguistic activities

Author: V. Turchin
Updated: Sep 1991
Filename: LINGACT.html

[Node to be completed]

Classification of languages by these two parameters of abstraction anf formalization leads to the following four types of language-related activities [Tur77].

~              Concrete language        Abstract language
~
~
Unformalized         Art                   Philosophy
language
~
~
Formalized         Descriptive              Theoretical
language           sciences                 sciences,
~                                           mathematics


Art is characterized by unformalized and concrete language. Words and language elements of other types are important only as symbols which evoke definite complexes of mental images and emotions. Philosophy is characterized by abstract informal thinking. The combination of high-level abstract constructs used in philosophy with a low degree of formalization requires great effort by the intuition and makes philosophical language the most difficult type of the four. Philosophy borders with art when it uses artistic images to stimulate the intuition. It borders with theoretical science when it develops conceptual frameworks to be used in construction of formal scientific theories. The language of descriptive science must be concrete and precise; formalization of syntax by itself does not play a large part, but rather acts as a criterion of the precision of semantics.

# Truth

Author: V. Turchin,
Updated: Oct 6, 1997
Filename: TRUTH.html

Since a statement is a generator of predictions, it is true if it generates only true predictions. A statement that does not produce any predictions is, by this definition, true -- vacuously. Most important statements can produce infinitely many predictions; we call such statements theories. We cannot directly verify such a statement -- we have to believe that it is true. Karl Popper stressed that we can never prove a theory, we only can reject it when it gives us false predictions. Creation of theories is, essentially, an evolutionary process. They arise as products of the creative human mind and compete for survival. Those producing more important predictions (and promising to give even more) are selected for usage; the others perish. (See Evolutionary approach to Epistemology and Epistemological Constructivism.) There is no criterion of truth other than its power to give predictions. Since powers, like multidimensional vectors, are hard to compare, there is no universal and absolute criterion of truth.

Proclamation of any truth as absolute because of being given through a "revelation" is sheer self-deception.

It is natural to test the validity of this approach to knowledge, meaning and truth in the field which does not allow imprecision and vagueness but requires a complete formalization and inambiguity, -- in mathematics. This is done in V.Turchin's Cybernetic foundation of mathematics. This approach gives answers to the century old questions about foundations of mathematics; in particular, a new and constructive interpretation of the full set theory is proposed there.

# Statement of infiniteness

Author: V. Turchin
Updated: Sep 1991
Filename: STATINF.html

[Node to be completed]

The statement that a certain process is infinite is not a prediction. But it is a generator of predictions. Our definition of a prediction T! includes a procedure, let it be denoted as P(t_i), which is applied to the states t_i, i= 1,2,... etc. of the test T and determines, in a finite time, whether each given state is Success, Failure, or neither of the two, which we shall denote as Not-end. Therefore, the statement that the state t_i is Not-end is a prediction, namely, the success of the procedure that runs P(t_i) and compares the result with Not-end. Now we can define what is the meaning of the statement that T is infinite: it is a generator which produces the following row of predictions:

t_1 is Not-end

t_2 is Not-end

t_3 is Not-end

...

etc., infinitely

# The law of the excluded middle

Author: V. Turchin
Updated: Sep 1991
Filename: EXMID.html

[Node to be completed]

We postulate and consider it self-evident that a test is either finite or infinite. This excludes any third, or middle, possibility, which gave this principle the name of the law of the excluded middle. From this we derive that a prediction may be only true or false. We further derive the law of the excluded middle for arbitrary statements. Consider some statement S which is a generator of predictions. Let us run this generator and define the following refutation process. Each time that a prediction if produced it it replaced by the symbol True if it is true and False if it is false. The first time the symbol False appears in the refutation process, the process ends and succeeds. This process is either finite, or infinite. If it is infinite, the statements S is true, otherwise it is false. No other outcome is possible.

# Predicate

Author: V. Turchin
Updated: Sep 1991
Filename: PREDICAT.html

[Node to be completed]

# Proof

Author: V. Turchin
Updated: Sep 1991
Filename: PROOF.html

[Node to be completed]

By proof of a statement S we mean any process which the subject of knowledge accepts as the evidence that S is true and is ready to use S for predictions on the basis of which to make decisions.

There are two cases of proof which can never arise any doubt because they do not base on any assumptions: verification and direct refutation.

When a statement cannot be directly verified or refuted, it is still possible that we take is as true relying on our intuition. For example, consider a test process T the stages of which can be represented by single symbols in some language. Let further the process T develop in such a manner that if the current stage is represented by the symbol A, then the next stage is also A, and the process comes to a successful end when the current stage is a different symbol, say B. This definition leaves us absolutely certain that T is infinite, even though this cannot be either verified, or refuted. In logic we call some most immediate of such statements axioms and use as a basis for establishing the truth of other statements. In natural sciences some of the self-evident truths serve as the very beginning of the construction of theories.

# Verification

Author: V. Turchin
Updated: Sep 1991
Filename: VERIFIC.html

[Node to be completed]

Verification is a direct use of the definition of truth to decide if the statement is true.

Predictions are, in principle, verifiable. You only have to initiate the test process that the prediction is about and wait until it comes to the final state. Thus, if a prediction is correct, there is, in principle, a way to verify this, even though it may require such a huge time, that in practice it is impossible.

Now consider the situation when you want to verify a prediction T!, but the test process T is infinite. This means that the prediction is false, but you will never be able to verify this directly. To sum up, the following three cases are possible when we try to verify a prediction:

       Testing process                  Prediction is
--------------------            ------------------
1.    ends in success                   verifyably true
2.    ends in failure                   verifyably false
3.       never ends             false, but this is not directly verifyable


As to the verififcation of general statements (generators of predictions), it may be possible only if the number of prediction produced is finite. In the more usual case, with an infinite number of prediction produced, the direct verification is impossible.

# Foundation of logic and mathematics

Author: V. Turchin
Updated: Sep 1991
Filename: LOGMATH.html

[Node to be completed]

...

In the cybernetic foundation of mathematics [...] we show that if we start with the

principle that a meaningful mathematical proposition is a generator of predictions, we come to the necessity of introducing the user of the mathematical tools into the formalism. The tools of mathematics do not work autonomously, like Turing machines, but rather in an interactive mode. The user of such machines is, like Einstein's observer, a form of "I".

# Probability

Author: C. Joslyn, V. Turchin
Updated: Aug 1993
Filename: PROBAB.html

[Node to be completed]

# Theories versus facts

Author: V. Turchin
Updated: Sep 1991
Filename: THEORIES.html

[Node to be completed]

We distinguish between factual statements and theories. If the path from a statement to verifiable predictions is short and uncontroversial, we call it factual. A theory is a statement which can generate a wide scope of predictions, but only through some intermediate steps, such as reasoning, computation, the use of other statements. Thus the path from a theory to predictions may not be unique and often becomes debatable. Between the extreme cases of statements that are clearly facts and those which are clearly theories there is a whole spectrum of intermediate cases.

Top-level theories of science are not deduced from observable facts; they are constructed by a creative act, and their usefulness can be demonstrated only afterwards. Einstein wrote: "Physics is a developing logical system of thinking whose foundations cannot be obtained by extraction from past experience according to some inductive methods, but come only by free fantasy".

The statement of the truth of a theory has essentially the same meaning as that of a simple factual judgment: we refer to some experience which justifies, or will justify, the decision-making on the basis of this statement. When this experience is in the past we say that the truth is established. When it is expected in the future we say it is hypothetical. There is no difference of principle between factual statements and theories: both are varieties of models of reality which we use to make decisions. A fact may turn out to be an illusion, or hallucination, or a fraud, or a misconception. On the other side, a well-established theory can be taken for a fact. And we should accept critically both facts and theories, and re-examine them whenever necessary. The differences between facts and theories are only quantitative: the length of the path from the statement to the production of predictions.

# Objectivity

Author: Turchin,
Updated: Sep 29, 1997
Filename: OBJECTIV.html

By objective description of the world we mean, first, a description in terms of some objects, and second, a description which is, as much as possible, objective'' in the usual sense, i.e. impersonal, not depending on the cognitive actions, or other features, of the subject of knowledge. The use of the same word in these two meanings is not accidental: a description can be objective'' because it is a description in terms of objects. Looking for objectivity is nothing else but factoring out certain cognitive actions. Function fa (see *Object*) factors out the action a by predicting what should be observed when the only change in the world is the subject's taking of the action a. If the prediction comes true, we interpret this as the same kind of stability as when nothing changes at all. The concept of object btings in a certain invariance, or stability, in the perception of a cybernetic system that actively explores its environment.

The metasystem transition from representations to their transformations is a step towards objectivity of knowledge. Actions and, in particular, sensations are intimately tied to the agent, the subject of knowledge. An object is a transformation and prediction of actions. The very fact that prediction is possible indicates that the transformation depends less on the subject of knowledge, the I', and more on the not-I'. This does not ensure a complete objectivity; alas, there is no such thing. But a jump from a representations to a transformation of representations verified by the practice of correct predictions, is the only way we know to increase objectivity.

When we perceive a tea-pot as an object, we have a lot of cognitive actions to factor out: we can walk around it, grasp it, rotate it in from of our eyes etc. But often we observe things from afar and that is about all we can do, as, fro instance, when we observe a star and still call it an object. Well, from the viewpoint of our theory, we always associate with an object some kind of stability, and stability exist only with respect to action. In the case of a star, this is the stability with respect to varying conditions of observation. We can observe the same' star at different times and factor out the differences in time by taking into account the rotation of the sky around the Earth's axis. The same is true with respect to the movement of the observer around the Earth's surface. The more we know of astronomy and physics, the greater number of properties of the object will we discover, such as the constancy of the star's spectrum etc.

We also must include into the concept of cognitive actions the more sophisticated and esoteric actions which were not among those actions for which evolution created human brain, but emerge as a result of the development of science. We get involved in this kind of actions when we construct huge accelerators of elementary particles and set up experiments to explore how the particles interact. As an apple and other physical bodies are invariants in the processing of input information by the brain, so an electron and other elementary particles are invariants of the scientific symbolic models of the world. We can measure the charge of the electron in many different ways -- which all are various cognitive actions -- but after making all the computations required by the theory, we still come to the same number (within the error). The same with mass, spin, etc. So an electron is, for us, an object, as real as an apple. One could qualify this statement by noticing that the existence of electrons depends on the legitimacy of our physical theory, which is not absolute. True enough. But who are we to claim that the legitimacy of our brain as a collection of models is absolute?

# Systems Concepts

Author: C. Joslyn, F. Heylighen, J. Bollen,
Updated: Jan 7, 1997
Filename: SYSCONC.html

Metasystem Transition Theory is founded on a thoroughly analyzed systems theoretic vocabulary of concepts and terms. This conceptual basis is intended to be both foundational and evolving, one aspect of a meta-foundational system.

The following list of nodes, linked to their definitions and cross-references, provides a first attempt to organize the fundamental concepts in the form of a semantic network. These nodes were developed by the PCP editors in August 1992, and originally implemented by Cliff Joslyn in HyperTeX, an extension of the TeX mathematical typesetting language with elementary hypertext capacities. They were converted to HTML by Johan Bollen in March 1994.

The following clickable map represents the semantic network formed by the collection of linked concepts. Links are represented by arrows, labeled with the link type. Unlabeled links are of the "is-a" type. Roots are shaded. Concepts with rectangles (not rounded) are clickable.

Many of the concepts referred to in these definitions are still undefined, and need to be developed later. Note that concepts for which there was no consensus on the definition have been split up into two (or more) concepts, tagged with the first letter of their author's last name. E.g. HJ-distinction is a definition of the "distinction" concept as proposed by Heylighen and Joslyn, T-distinction is the corresponding definition proposed by Turchin. Different senses of the same word have been tagged with subsequent numbers to distinguish them. This list of terms gives a first idea of the process of semantic analysis which is at the basis of PCP development.

# Foundation of Mathematics

Author: Turchin,
Updated: Jun 24, 1997
Filename: FOUNDMAT.html

The philosophy developed in MST theory leads to a certain view on the nature and workings of mathematics and gives certain answers to the long-standing questions about the foundations of mathematics. At present we have two documents on the foundation of logic and mathematics in the light of our epistemology and ontology:

1. Valentin F. Turchin, The Cybernetic Foundation of Mathematics, Technical report (170 pages) of the City College, City University of New York, 1983.
2. Valentin F. Turchin, A constructive interpretation of the full set theory. The Journal of Symbolic Logic, 52, pp. 172-201, 1987.
We plan to represent the contents of these documents in the format of Principia Cybernetica. We hope that the work along these lines will be discussed and continued.

The concept of the metasystem transition in formal systems is applied in a research program on computer program optimization through partial and lazy evaluation methods, culminating in a Supercompiler. For more information, see the following review paper:

# What is complexity?

Author: F. Heylighen,
Updated: Dec 9, 1996
Filename: COMPLEXI.html

Complexity has turned out to be very difficult to define. The dozens of definitions that have been offered all fall short in one respect or another, classifying something as complex which we intuitively would see as simple, or denying an obviously complex phenomenon the label of complexity. Moreover, these definitions are either only applicable to a very restricted domain, such as computer algorithms or genomes, or so vague as to be almost meaningless. (1996) gives a good review of the different definitions and their shortcomings, concluding that complexity necessarily depends on the language that is used to model the system. Still, I believe there is a common, "objective" core in the different concepts of complexity. Let us go back to the original Latin word complexus, which signifies "entwined", "twisted together". This may be interpreted in the following way: in order to have a complex you need two or more components, which are joined in such a way that it is difficult to separate them. Similarly, the Oxford Dictionary defines something as "complex" if it is "made of (usually several) closely connected parts". Here we find the basic duality between parts which are at the same time distinct and connected. Intuitively then, a system would be more complex if more parts could be distinguished, and if more connections between them existed. More parts to be represented means more extensive models, which require more time to be searched or computed. Since the components of a complex cannot be separated without destroying it, the method of analysis or decomposition into independent modules cannot be used to develop or simplify such models. This implies that complex entities will be difficult to model, that eventual models will be difficult to use for prediction or control, and that problems will be difficult to solve. This accounts for the connotation of difficult, which the word "complex" has received in later periods.

The aspects of distinction and connection determine two dimensions characterizing complexity. Distinction corresponds to variety, to heterogeneity, to the fact that different parts of the complex behave differently. Connection corresponds to constraint, to redundancy, to the fact that different parts are not independent, but that the knowledge of one part allows the determination of features of the other parts. Distinction leads in the limit to disorder, chaos or entropy, like in a gas, where the position of any gas molecule is completely independent of the position of the other molecules. Connection leads to order or negentropy, like in a perfect crystal, where the position of a molecule is completely determined by the positions of the neighbouring molecules to which it is bound. Complexity can only exist if both aspects are present: neither perfect disorder (which can be described statistically through the law of large numbers), nor perfect order (which can be described by traditional deterministic methods) are complex. It thus can be said to be situated in between order and disorder, or, using a recently fashionable expression, "on the edge of chaos".

The simplest way to model order is through the concept of symmetry, i.e. invariance of a pattern under a group of transformations. In symmetric patterns one part of the pattern is sufficient to reconstruct the whole. For example, in order to reconstruct a mirror-symmetric pattern, like the human face, you need to know one half and then simply add its mirror image. The larger the group of symmetry transformations, the smaller the part needed to reconstruct the whole, and the more redundant or "ordered" the pattern. For example, a crystal structure is typically invariant under a discrete group of translations and rotations. A small assembly of connected molecules will be a sufficient "seed", out of which the positions of all other molecules can be generated by applying the different transformations. Empty space is maximally symmetric or ordered: it is invariant under any possible transformation, and any part, however small, can be used to generate any other part.

It is interesting to note that maximal disorder too is characterized by symmetry, not of the actual positions of the components, but of the probabilities that a component will be found at a particular position. For example, a gas is statistically homogeneous: any position is as likely to contain a gas molecule as any other position. In actuality, the individual molecules will not be evenly spread. But if we look at averages, e.g. the centers of gravity of large assemblies of molecules, because of the law of large numbers the actual spread will again be symmetric or homogeneous. Similarly, a random process, like Brownian motion, can be defined by the fact that all possible transitions or movements are equally probable.

Complexity can then be characterized by lack of symmetry or "symmetry breaking", by the fact that no part or aspect of a complex entitity can provide sufficient information to actually or statistically predict the properties of the others parts. This again connects to the difficulty of modelling associated with complex systems.

(1996) notes that the definition of complexity as midpoint between order and disorder depends on the level of representation: what seems complex in one representation, may seem ordered or disordered in a representation at a different scale. For example, a pattern of cracks in dried mud may seem very complex. When we zoom out, and look at the mud plain as a whole, though, we may see just a flat, homogeneous surface. When we zoom in and look at the different clay particles forming the mud, we see a completely disordered array. The paradox can be elucidated by noting that scale is just another dimension characterizing space or time (Havel, 1995), and that invariance under geometrical transformations, like rotations or translations, can be similarly extended to scale transformations (homotheties).

Havel (1995) calls a system "scale-thin" if its distinguishable structure extends only over one or a few scales. For example, a perfect geometrical form, like a triangle or circle, is scale-thin: if we zoom out, the circle becomes a dot and disappears from view in the surrounding empty space; if we zoom in, the circle similarly disappears from view and only homogeneous space remains. A typical building seen from the outside has distinguishable structure on 2 or 3 scales: the building as a whole, the windows and doors, and perhaps the individual bricks. A fractal or self-similar shape, on the other hand, has infinite scale extension: however deeply we zoom in, we will always find the same recurrent structure. A fractal is invariant under a discrete group of scale transformations, and is as such orderly or symmetric on the scale dimension. The fractal is somewhat more complex than the triangle, in the same sense that a crystal is more complex than a single molecule: both consist of a multiplicity of parts or levels, but these parts are completely similar.

To find real complexity on the scale dimension, we may look at the human body: if we zoom in we encounter complex structures at least at the levels of complete organism, organs, tissues, cells, organelles, polymers, monomers, atoms, nucleons, and elementary particles. Though there may be superficial similarities between the levels, e.g. between organs and organelles, the relations and dependencies between the different levels are quite heterogeneous, characterized by both distinction and connection, and by symmetry breaking.

We may conclude that complexity increases when the variety (distinction), and dependency (connection) of parts or aspects increase, and this in several dimensions. These include at least the ordinary 3 dimensions of spatial, geometrical structure, the dimension of spatial scale, the dimension of time or dynamics, and the dimension of temporal or dynamical scale. In order to show that complexity has increased overall, it suffices to show, that - all other things being equal - variety and/or connection have increased in at least one dimension.

The process of increase of variety may be called differentiation, the process of increase in the number or strength of connections may be called integration. We will now show that evolution automatically produces differentiation and integration, and this at least along the dimensions of space, spatial scale, time and temporal scale. The complexity produced by differentiation and integration in the spatial dimension may be called "structural", in the temporal dimension "functional", in the spatial scale dimension "structural hierarchical", and in the temporal scale dimension "functional hierarchical".

It may still be objected that distinction and connection are in general not given, objective properties. Variety and constraint will depend upon what is distinguished by the observer, and in realistically complex systems determining what to distinguish is a far from trivial matter. What the observer does is picking up those distinctions which are somehow the most important, creating high-level classes of similar phenomena, and neglecting the differences which exist between the members of those classes (Heylighen, 1990). Depending on which distinctions the observer makes, he or she may see their variety and dependency (and thus the complexity of the model) to be larger or smaller, and this will also determine whether the complexity is seen to increase or decrease.

For example, when I noted that a building has distinguishable structure down to the level of bricks, I implicitly ignored the molecular, atomic and particle structure of those bricks, since it seems irrelevant to how the building is constructed or used. This is possible because the structure of the bricks is independent of the particular molecules out of which they are built: it does not really matter whether they are made out of concrete, clay, plaster or even plastic. On the other hand, in the example of the human body, the functioning of the cells critically depends on which molecular structures are present, and that is why it is much more difficult to ignore the molecular level when building a useful model of the body. In the first case, we might say that the brick is a "closed" structure: its inside components do not really influence its outside appearance or behavior (Heylighen, 1990). In the case of cells, though, there is no pronounced closure, and that makes it difficult to abstract away the inside parts.

Although there will always be a subjective element involved in the observer's choice of which aspects of a system are worth modelling, the reliability of models will critically depend on the degree of independence between the features included in the model and the ones that were not included. That degree of independence will be determined by the "objective" complexity of the system. Though we are in principle unable to build a complete model of a system, the introduction of the different dimensions discussed above helps us at least to get a better grasp of its intrinsic complexity, by reminding us to include at least distinctions on different scales and in different temporal and spatial domains.

# Foundational Concepts

Author: C. Joslyn,
Updated: Jan 1992
Filename: FOUNDCONC.html

[Node to be completed]

In particular, we regard the following concepts as utterly fundamental to, inseparable within, and inextricably interrelated in any system of thought which can be described as "cybernetic". As (current) foundations they can be described, but not defined in terms of other concepts. As such they are essentially and necessarily undefined. While these three concepts form the (current) foundation for Metasystem Transition Theory, the following subsections detail fundamental concepts which follow directly from these.

## Actions

Actions are the fundamentals of reality in Metasystem Transition Theory. Our philosophy is process oriented, where change and evolution are emphasized over objects, entities, and static properties. This is fundamentally in keeping with other process philosophies \cite{WHA29} and much of cybernetic theory \cite{?} (see section metaphysics).

## Distinctions

We use the concept of the distinction as the fundamental nominal notion, as the generator of all separateness, differentiation, of

all categories and dimensions, indeed of all knowledge.

Of course this concept has a long and venerable history not only in general philosophy, but particularly in Cybernetics and Systems Science. Distinctions are recognized as fundamental by Bateson \cite{BAG72c,BAG72e}, and were developed into an elegant (if usually misunderstood) formal language of systems by Spencer-Brown and others \cite{GOJVAF79,SPG,VAF75} \both{I need a ref for S-Brown}.

Distinct categories are necessary for all communication and for all information theory, if not all mathematics. Furthermore, the distinction between discrete distinctions and continuous differences is a

perennial issue in all Cybernetics and Systems Science. More recently, Heylighen has developed a detailed theory of causality and action in terms of distinctions \cite{HEF89b,HEF90d} which informs a great deal of Metasystem Transition Theory.

## Subject

The subject of Metasystem Transition Theory, what Metasystem Transition Theory is about, is general

philosophy from a cybernetic perspective. But Metasystem Transition Theory itself is also an object, i.e.\ a linguistic construction almost entirely in the English

language. As such, both Principia Cybernetica and Metasystem Transition Theory must be always recognized as existing relative to that interpretive framework.

But in the same way, all constructions of the human mind, indeed all systems, are relativized to and embedded within particular interpretive perspectives. We call the presence of such a perspective the presence of the subject. The involvement of an explicit subject in all aspects of Metasystem Transition Theory draws our attention to the level-relativity of theory and explanation, and the inside/outside distinctions such as system/environment.

The inside and outside are complementary, irreducible perspectives. Everything viewed from the inside (subjective) looks and is described differently from the outside (objective). From the inside perspective the subject appears as God, nature, or chance; from the outside perspective (when it is available to us) the subject appears as a willing agent. In physics, the subject is called the observer, and the result is quantum complementarity. In Turchin's formal theory \cite{TUV87a}, the subject is called the user of the cybernetic machine.

# Evolutionary Theory

Author: F. Heylighen,
Updated: Jan 27, 1997
Filename: EVOLUT.html

We see evolution as based on the trial-and-error process of variation and natural selection of systems at all levels of complexity. The name of 'natural selection' comes from the Darwinian theory of biological evolution, which distinguishes "natural" selection from "artificial" selection, where specific features are retained or eliminated depending on a goal or intention (e.g. the objective of a cattle breeder who would like to have cows that produce more milk). The "implicit goal" of natural selection is maintenance or reproduction of a configuration at some level of abstraction. The selection is natural in the sense that there is no actor or purposive system making the selection. The selection we are discussing is purely automatic or spontaneous, without plan or design involved.

Evolution typically leads to greater complexity, although one must be careful how one defines complexity.

## Selection or self-organization?

Many criticisms have been and are being raised against the Darwinian view of evolution. We will here not discuss the criticisms stating that there are designs or plans guiding evolution, but focus on a more recent upsurge of people, many of whom are associated with the systems movement, who state that natural selection must be complemented by self-organization in order to explain evolution. (see e.g. Jantsch, 1979; Kauffman, 1993; Swenson, 19). However, we must not confuse the specific theory of Darwinian evolution with the general principle of natural selection.

The narrow or specific interpretation of Darwinism sees evolution as the result of selection by the environment acting on a population of organisms competing for resources. The winners of the competition, those who are most fit to gain the resources necessary for survival and reproduction, will be selected, the others are eliminated. Even when abstracting from the fact that we are speaking about "organisms", this view of evolution entails two strong restriction:

1. it assumes that there is a multitude ("population") of configurations undergoing selection;
2. it assumes that selection is carried out by their common environment.
Like Swenson (19) notes, it cannot explain the evolution of a "population of one". In our present, more general interpretation, there is no need for competition between simultaneously present configurations. A configuration can be selected or eliminated independently of the presence of other configurations: a single system can pass through a sequence of configurations, some of which are retained while others are eliminated (see the Principle of Selective Retention). The only "competition" involved is one between subsequent states of the same system. Such selection can still be "natural".

More importantly this selection does not in any way presuppose the existence of an environment external to the configuration undergoing selection. It is easy enough to imagine configurations that are intrinsically stable or unstable. A cloud of gas molecules in a vacuum (i.e. an "empty" environment) will diffuse, independently of any outside forces. A crystal in the same vacuum will retain its rigid crystalline structure. The first configuration (the cloud) is eliminated, the second one maintains. The stability of the structure, functioning as a selection criterion, is purely internal to the configuration: no outsides forces or pressures are necessary to explain them.

In cases like these, the selection is inherent in the configuration itself, and an asymmetric transition from varying to stable may be called "self-organization". In the present view, "natural selection" encompasses both external, Darwinian selection, and internal, "self-organizing" selection.

# Self-organization

Author: F. Heylighen
Updated: Jan 27, 1997
Filename: SELFORG.html

 Synopsys: Self-organization is a process where the organization (constraint, redundancy) of a system spontaneously increases, i.e. without this increase being controlled by the environment or an encompassing or otherwise external system

Self-organization is a basically a process of evolution where the effect of the environment is minimal, i.e. where the development of new, complex structures takes place primarily in and through the system itself. As argued in the section on evolutionary theory, self-organization can be understood on the basis of the same variation and natural selection processes as other, environmentally driven processes of evolution. Self-organization is normally triggered by internal variation processes, which are usually called "fluctuations" or "noise". The fact that these processes produce a selective retained ordered configuration has been called the "order from noise" principle by Heinz von Foerster, and the "order through fluctuations" mechanism by Ilya Prigogine. Both are special cases of what I have proposed to call the principle of selective variety.

The increase in organization can be measured more objective as a decrease of statistical entropy (see the Principle of Asymmetric Transitions). This is again equivalent to an increase in redundancy, information or constraint: after the self-organization process there is less ambiguity about which state the system is in. A self-organizing system which also decreases its thermodynamical entropy must necessarily (because of the second law of thermodynamics) export ("dissipate") such entropy to its surroundings, as noted by von Foerster and Prigogine. Prigogine called systems which continuously export entropy in order to maintain their organization dissipative structures.

Self-organization is usually associated with more complex, non-linear phenomena, rather than with the relatively simple processes of structure maintenance of diffusion. All the intricacies (limit cycles, chaos, sensitivity to initial conditions, dissipative structuration, ...) associated with non-linearity can simply be understood through the interplay of positive and negative feedback cycles: some variations tend to reinforce themselves (see Autocatalytic Growth), others tend to reduce themselves. Both types of feedback fuel natural selection: positive feedback because it increases the number of configurations (up to the point where resources become insufficient), negative feedback because it stabilizes configurations. Either of them provides the configuration with a selective advantage over competing configurations. The interaction between them (variations can be reinforced in some directions while being reduced in others) may create intricate and unpredictable patterns, which can develop very quickly until they reach a stable configuration (attractor).

# Entropy and the Laws of Thermodynamics

Author: J. de Rosnay
Updated: Jul 3, 1998
Filename: ENTRTHER.html

The principal energy laws that govern every organization are derived from two famous laws of thermodynamics. The second law, known as Carnot's principle, is controlled by the concept of entropy.

Today the word entropy is as much a part of the language of the physical sciences as it is of the human sciences. Unfortunately, physicists, engineers, and sociologists use indiscriminately a number of terms that they take to be synonymous with entropy, such as disorder, probability, noise, random mixture, heat; or they use terms they consider synonymous with antientropy, such as information, neguentropy, complexity, organization, order, improbability.

There are at least three ways of defining entropy:

• in terms of thermodynamics (the science of heat), where the names of Mayer, Joule, Carnot, and Clausius (1865) are important;
• in terms of statistical theory, which fosters the equivalence of entropy and disorder -- as a result of the work of Maxwell, Gibbs, and Boltzmann (1875), and
• in terms of information theory, which demonstrates the equivalence of neguentropy (the opposite of entropy) and information -- as a result of the work of Szilard, Gabor, Rothstein, and Brillouin (1940-1950)
The two principal laws of thermodynamics apply only to closed systems, that is, entities with which there can be no exchange of energy, information, or material. The universe in its totality might be considered a closed system of this type; this would allow the two laws to be applied to it.

The first law of thermodynamics says that the total quantity of energy in the universe remains constant. This is the principle of the conservation of energy. The second law of thermodynamics states that the quality of this energy is degraded irreversibly. This is the principle of the degradation of energy.

The first principle establishes the equivalence of the different forms of energy (radiant, chemical, physical, electrical, and thermal), the possibility of transformation from one form to another, and the laws that govern these transformations. This first principle considers heat and energy as two magnitudes of the same physical nature

About 1850 the studies of Lord Kelvin, Carnot, and Clausius of the exchanges of energy in thermal machines revealed that there is a hierarchy among the various forms of energy and an imbalance in their transformations. This hierarchy and this imbalance are the basis of the formulation of the second principle.

In fact physical, chemical, and electrical energy can be completely changed into heat. But the reverse (heat into physical energy, for example) cannot be fully accomplished without outside help or without an inevitable loss of energy in the form of irretrievable heat. This does not mean that the energy is destroyed; it means that it becomes unavailable for producing work. The irreversible increase of this nondisposable energy in the universe is measured by the abstract dimension that Clausius in 1865 called entropy (from the Greek entrope, change).

The concept of entropy is particularly abstract and by the same token difficult to present. Yet some scientists consider it intuitively; they need only refer mentally to actual states such as disorder, waste, and the loss of time or information. But how can degraded energy, or its hierarchy, or the process of degradation be truly represented?

There seems to be a contradiction between the first and second principles. One says that heat and energy are two dimensions of the same nature; the other says they are not, since potential energy is degraded irreversibly to an inferior, less noble, lower-quality form--heat. Statistical theory provides the answer. Heat is energy; it is kinetic energy that results from the movement of molecules in a gas or the vibration of atoms in a solid. In the form of heat this energy is reduced to a state of maximum disorder in which each individual movement is neutralized by statistical laws.

Potential energy, then, is organized energy; heat is disorganized energy. And maximum disorder is entropy. The mass movement of molecules (in a gas, for example) will produce work (drive a piston). But where motion is ineffective on the spot and headed in all directions at the same time, energy will be present but ineffective. One might say that the sum of all the quantities of heat lost in the course of all the activities that have taken place in the universe measures the accumulation of entropy.

One can generalise further. Thanks to the mathematical relation between disorder and probability, it is possible to speak of evolution toward an increase in entropy by using one or the other of two statements: "left to itself, an isolated system tends toward a state of maximum disorder" or "left to itself, an isolated system tends toward a state of higher probability." These equivalent expressions can be summarized:

• Potential energy -> entropy
• Ordered energy -> disorganized energy (heat)
• High-quality energy -> heat (low-grade energy)
• Order -> disorder
• Improbability -> probability

The concepts of entropy and irreversibility, derived from the second principle, have had a tremendous impact on our view of the universe. In breaking the vicious circle of repetitiveness in which the ancients were trapped, and in being confronted with biological evolution generating order and organization, the concept of entropy indirectly opens the way to a philosophy of progress and development. At the same time it introduces the complementarity between the "two great drifts of the universe" described in the works of Bergson and Teilhard de Chardin.

The image of the inexorable death of the universe, as suggested by the second principle, has profoundly influenced our philosophy, our ethics, our vision of the world, and even our art. The thought that by the very nature of entropy the ultimate and only possible future for man is annihilation has infiltrated our culture like a paralysis. This consideration led Leon Brillouin to ask, "How is it possible to understand life when the entire world is ordered by a law such as the second principle of thermodynamics, which points to death and annihilation?"

# The trial-and-error method

Author: F. Heylighen, & V. Turchin,
Updated: Aug 6, 1996
Filename: TRIALERR.html

 Synopsys: different possible configurations are generated, after a test of their "fitness", the good ones are retained, and the bad ones or "errors" are eliminated

According to the neo-Darwinist view, evolution takes place through the creation of random combinations of matter, with the subsequent struggle for existence, as a result of which some combinations survive and proliferate, while other perish. Popper describes this as the work of the general method of trial and error-elimination. Campbell uses the term blind variation and selective retention. Newell and Simon in their theory of problem solving called this mechanism "generate and test". Here we will speak simply of the trial and error method.

We do not need to use the term blind', because in cultural evolution or in problem solving we often have informed and guided choices. But even with regard to biological evolution we cannot be sure, much less prove, that the variation is blind. It is true that we build our theory and check it against facts in the assumption that variations are blind. But we do not really use the fact that the variation is, indeed, blind or random, i.e. all choices physically and logically possible are equiprobable. The success of the theory proves that blindness, at the present state of the theory, is sufficient, but does not prove it is necessary. The main requirement is that a large number of possible states or solutions is explored through a process of variation.

The principle is so powerful that any type of variation or trial, whether guided by foreknowledge or not, followed by the elimination of the "bad", or "unfit" trials, and the retention or propagation of the "fit" trials, will result in evolution. The fact that succesful steps are retained leads to an irreversible accumulation, a "ratchet effect" which only allows movement in a particular general direction, without going back (see the principle of asymmetric transitions).

# Variety

Author: C. Joslyn,
Updated: Jan 1992
Filename: VARIETY.html

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Variety has always been a fundamental idea in Cybernetics and Systems Science, and is so in Metasystem Transition Theory. Variety is defined as a multiplicity of distinctions. The existence of variety is necessary for all change, choice, and information.

Frequently the quantity of variety and the change in the quantity of variety (positive increase or negative decrease) is critical to understand system evolution. Where variety is manifest in a process, then we sometimes want to say that there is uncertainty about the outcome of the process; when that uncertainty is relieved but the occurrence of one of the possibilities, then we gain information. The are many possible ways to measure the quantity of variety, uncertainty, or information \cite{KLGFOT87}. The simplest is the count of the number of distinctions. More useful can be the logarithm of that number as a quantity of information, which is called the Hartley entropy. When sets and subsets of distinctions are considered, possibilistic nonspecificities result \cite{KLG84a}. The most celebrated are the stochastic entropies of classical information theory, which result from applying probabilistic distributions to the various distinctions. A reduction in the quantity of variety is the process of selection.

# Selection

Author: F. Heylighen, C. Joslyn,
Updated: Aug 5, 1996
Filename: SELECT.html

A reduction in the quantity of variety is the process of selection: some of the possibilities or alternatives are eliminated, others are retained. The result is a constraint: a limitation of the number of possibilities.

Selection processes are endemic in systems theories. The most significant is the natural selection of Darwinian evolution, but in this context we can recognize natural selection outside of the context of biological evolution as any selection process which eliminates distinctions. Also included in the concept of selection are all forms of stability and equilibrium.

Variation on its own, without further constraints, produces entropy or disorder, by diffusion of existing constraints or dependencies. The equivalent for DNA is called "genetic drift".

However, variation is generally held in check by selection. Selection is the elimination or reduction of part of the variety of configurations produced by variation. Selection decreases disorder or entropy, by reducing the number of possibilities (Heylighen, 1992). A system that undergoes selection is constrained: it is restricted in the number of variations it can maintain. The existence of selection follows from the fact that in general not all variants are equivalently stable or capable of (re)production: those that are more easy to maintain or generate will become more numerous relative to the others (Heylighen, 1992). If all possible configurations are equally likely to be produced or conserved, there is no selection, and the only possible outcome of the process is maximization of statistical entropy, as in the cloud of gas molecules that diffuses to homogeneously fill its container. Selection can be internal, as when an unstable system (e.g. a radio-active atom) spontaneously annihilates, or external, as when a system is eliminated because it is not adapted to its environment (Heylighen, 1991a). Fitness is a measure of the likeliness that a configuration will be selected.

# Fitness

Author: F. Heylighen,
Updated: Aug 5, 1996
Filename: FITNESS.html

Fitness is an assumed property of a system that determines the probability that that system will be selected, i.e. that it will survive, reproduce or be produced. Technically, the fitness of a system can be defined as the average number of instances of that system that can be expected at the next time step or "generation", divided by the present number of instances. Fitness larger than one means that the number of systems of that type can be expected to increase. Fitness smaller than one means that that type of system can eventually be expected to disappear, in other words that that type of system will be eliminated by selection. (see a definition of fitness with transition probabilities) High fitness can be achieved if a system is very stable, so that it is unlikely to disappear, and/or if it is likely that many copies of that system will be produced, by replication or by independent generation of similar configurations (for example, though snow flakes are unstable and cannot reproduce, they are still likely to be recurrently produced under the right circumstances). The fitter a configuration, the more likely it is to be encountered on future occasions (Heylighen, 1994).

Although this technical interpretation may seem rather far removed from the intuitive notion, the English word "fit" is eminently suited for expressing the underlying dynamic. Its spectrum of meanings ranges between two poles: 1) "fit" as "strong", "robust", "in good condition"; 2) "fit" as "adapted to", "suited for", "fitting". The first sense, which may be called "absolute fitness", points to the capability to survive internal selection, i.e. intrinsic stability and capacity for (re)production. The second sense, which may be called "relative fitness", refers to the capability to survive external selection, i.e. to cope with specific environmental perturbations or make use of external resources.

It must be noted that "internal" and "external" merely refer to complementary views of the same phenomenon. What is internal for a whole system, may be external for its subsystems or components. For example, the concentration of oxygen in the air is an external selective factor for animals, since in order to survive they need a respiratory system fit to extract oxygen. Similarly, the concentration of carbon dioxide is an external selective factor for plants. However, when we consider the global ecosystem consisting of plants and animals together, we see that the concentrations of both oxygen and carbon dioxide are internally determined, since oxygen is produced out of carbon dioxide by plants, and carbon dioxide out of oxygen by animals. Survival of the global system requires an internal "fit" of the two halves of the carbon dioxide - oxygen cycle: if more oxygen or carbon dioxide would be consumed than produced, the whole system would break down.

Similarly, when we look at a crystal as whole system, we see it as a stable structure that is unlikely to disintegrate, i.e. it is absolutely fit and survives internal selection. However, when we look at the molecules as the parts that make up the crystal, we see that they must have the right connections or bonds, i.e. fit relations, to form a stable whole. The exact configuration of each molecule is externally selected by the other molecules to which it must fit. In this way, every absolute or intrinsic fitness characterizing a whole can be analysed as the result of a network of interlocking relational fitnesses connecting the parts .

In summary, a system will be selected if: 1) its parts "fit together", i.e. form an intrinsically stable whole, 2) the whole "fits" its environment, i.e. it can resist external perturbations and profit from external resources to (re)produce.

# Survival

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Aug 1993
Filename: SURVIV.html

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# Mathematical Modeling of Evolution

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: MATHME.html

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Biological evolution is a very complex process. Using mathematical modeling, one can try to clarify its features. But to what extent can that be done? For the case of evolution, it seems unrealistic to develop a detailed and fundamental description of phenomena as it is done in theoretical physics. Nevertheless, what can we do? Can mathematical models help us to systemize our knowledge about evolution? Can they provide us with more a profound understanding of the particularities of evolution? Can we imagine (using mathematical representation) some hypothetical stages of evolution? Can we use mathematical models to simulate some kind of artificial "evolution"?

In order to clarify such questions, it is natural to review the already developed models in a systematic manner. In evolutionary modeling one can distinguish the following branches:

• Models of molecular-genetic systems origin have been constructed in connection with the origin of life problem. Quasispecies and hypercycles by M. Eigen and P. Schuster and sysers by V.A. Ratner and V.V. Shamin are the best known. These models describe mathematically some hypothetical evolutionary stages of prebiological self-reproducing macromolecular systems.
• General models of evolution describe some informational and cybernetic aspects of evolution. The neutral evolution theory by M. Kimura and S. Kauffman's automata are profound examples of such models.
• Artificial evolution models (genetic algorithms by J.H. Holland, applied evolutionary modeling by L.Fogel and by his followers) try to apply the evolutionary principles to practical optimization problems.

# Models of molecular-genetic systems origin

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: ORIGIN.html

>In the 1970s, Manfred Eigen launched a very impressive research attack on the life origin problem [1,2]. M. Eigen and his coworkers tried to imagine the transient stages between the molecular chaos in a prebiotic soup and simple macromolecular self-reproducing systems. They developed several mathematical models, illustrating the hypothetical macromolecular systems; quasispecies and hypercycles are the most significant. The models were intensively analyzed mathematically as well as compared with biochemical experiments and discussed from different points of view.

Quasispeciesis a model of a Darwinian evolution of a population of the polynucleotide strings (analogous to RNA), which are supposed to have certain replication abilities. Polynucleotide strings are modeled by informational sequences. The model implies that there is a master sequence, having a maximal selective value (a selective value is a sequence fitness). The selective values of other sequences are defined as follows: the more similar is the particular sequence to the master sequence, the greater is its selective value. The evolution process includes the selection (according to the selective values) and the mutations of informational sequences. The final result of an evolution is a quasispecie, that is the distribution of sequences in the neighborhood of the master sequence.

The model of quasispecies was analyzed mathematically by a number of authors [1-6], and the particularities, concerning a final sequence distribution, a restriction on the sequence length, and a rate of evolution were quantitatively estimated. A short review of these mathematical results as well as a formal description of the model are given in the child node quasispecies.

M.Eigen et al interpreted quasispecies as a model of a primitive RNA-sequences origin. Taking into account the nucleotide coupling abilities, they deduced that the length of these primitive RNA-sequences could not be greater than 100 nucleotides. As the further stage of macromolecular evolution, M.Eigen and P.Schuster proposed a model of hypercycles, which include the polynucleotide RNA-sequences as well as polypeptide enzymes [2].

Hypercycle is a self-reproducing macromolecular system, in which RNAs and enzymes cooperate in the following manner: there are n RNAs; i-th RNA codes i-th enzyme (i = 1,2,...,n); the enzymes cyclically increase RNA's replication rates, namely, 1-st enzyme increases replication rate of 2-nd RNA, 2-nd enzyme increases replication rate of 3-rd RNA, ..., n-th enzyme increases replication rate of 1-st RNA; in addition, the described system possesses primitive translation abilities, so the information stored in RNA-sequences is translated into enzymes, analogously to the usual translation processes in biological objects. For effective competition (i.e. for surviving the hypercycle with greatest reaction rate and accuracy), the different hypercycles should be placed in separate compartments, for example into A.I.Oparin's coacervates [7]. M.Eigen and P.Schuster consider hypercycles as predecessors of protocells (primitive unicellular biological organisms) [2]. As quasispecies, hypercycles were also analyzed mathematically in details. The child node hypercycles describes formally this model.

The models of quasispecies and hypercycles were rather well developed and provide certain consequent description of hypothetical process of first molecular-genetic system origin. But they are not unique. The life origin problem is very intriguing, and there is diversity of this problem investigations. We mention here only some of them.

F.H.C.Crick discussed in details the possible steps of genetic code origin [8].

F.J.Dyson proposed the model of "phase transition from chaos to order" to interpret the stages of cooperative RNA-enzyme systems origin [9].

P.W.Anderson et al used the physical spin-glass concept in order to model a simple polynucleotide sequence evolution [10]. This model is similar to the quasispecies.

H.Kuhn considered the creation of the aggregates of RNAs as a stage for an origin of a simple ribosome-like translation device [11].

So, the approaches to the molecular-genetic system origin modeling are different in many relations. However, some convergence points do exist. In the early 1980s, V.A.Ratner and V.V.Shamin (Novosibirsk, Russia), D.H.White (California), and R.Feistel (Berlin) independently proposed the same model [12-14]. V.A.Ratner called it "syser" (the abbreviation from SYstem of SElf-Reproduction).

Syser is a system of catalytically interacting macromolecules, it includes the polynucleotide matrix and several proteins; there are two obligatory proteins: the replication enzyme and the translation enzyme; syser can also include some structural proteins and additional enzymes. The polynucleotide matrix codes proteins, the replication enzyme provides the matrix replication process, the translation enzyme provides the protein synthesis according to an information coded in the matrix; structural proteins and additional enzymes can provide optional functions. Analogously to hypercycles, different sysers should be inserted into different compartments for effective competition. Mathematical description of sysers (see Sysers for details) is similar to that of hypercycles.

As compared with hypercycles, sysers are more similar to simple biological organisms. The concept of sysers provides the ability to analyze evolutionary stages from a mini-syser, which contains only matrix and replication and translation enzymes, to protocells, having rather real biological features. For example, "Adaptive syser" [15] includes a simple molecular control system, which "turns on" and "turns off" synthesis of some enzyme in response to the external medium change; the scheme of this molecular regulation is similar to the classical model by F.Jacob and J.Monod [16]. The control system of the adaptive syser could be the first control system, which was "invented" by biological evolution.

Additionally, it should be noted, that the scheme of sysers is similar to that of the Self-Reproducing Automata, which were proposed and investigated at the sunrise of modern computer era by J.von Neumann [17].

Conclusion. The considered models of course can't explain the real life origin process, because these models are based on various plausible assumptions rather than on a strong experimental evidences. Nevertheless, quasispecies, hypercycles, and sysers provide a well defined mathematical background for understanding of the first molecular-genetic systems evolution. These models can be used to develop the scenarios of the first cybernetic systems origin, they can be juxtaposed with biochemical data to interpret qualitatively the corresponding experiments, and can be considered as a step for developments of more powerful models.

References:

1. M.Eigen. Naturwissenshaften. 1971. Vol.58. P. 465.

2. M.Eigen, P.Schuster. "The hypercycle: A principle of natural self-organization". Springer Verlag: Berlin etc. 1979.

3. C.J.Tompson, J.L.McBride. Math. Biosci. 1974. Vol.21. P.127.

4. B.L.Jones, R.H.Enns, S.S. Kangnekar. Bull. Math. Biol. 1976. Vol.38. N.1. P.15.

5. Von H.D.Fosterling, H.Kuhn, K.H.Tews. Ang. Chem. 1972. Jg.84. Nr.18. S. 862.

6. V.G.Red'ko. Biofizika. 1986. Vol. 31. N.3. P. 511 (In Russian).

7. A.I.Oparin. "The origin of life". New York, 1938. A.I.Oparin. "Genesis and evolutionary development of life". New York, 1968.

8. F.H.C.Crick. J. Mol. Biol. 1968. Vol. 38. N.3. P.367.

9. F.J.Dyson. J. Mol. Evol. 1982. Vol.18. N.5. P.344.

10. P.W.Anderson. Proc. Natl. Acad. Sci. USA. 1983. V.80. N.11. P.3386. Rokhsar D.S., Anderson P.W., Stein D.L. J.Mol. Evol. 1986. V.23. N.2. P.119.

11. H.Kuhn. Angew. Chem. 1972. Jg.84. Nr.18. S.838.

12. V.A.Ratner and V.V.Shamin. In: Mathematical models of evolutionary genetics. Novosibirsk: ICG, 1980. P.66. V.A.Ratner and V.V.Shamin. Zhurnal Obshchei Biologii. 1983. Vol.44. N.1. PP. 51. (In Russian).

13. D.H.White. J. Mol. Evol. 1980. Vol.16. N.2. P.121.

14. R.Feistel. Studia biophysica.1983. Vol.93. N.2. P.113.

15. V.G.Red'ko. Biofizika. 1990. Vol. 35. N.6. P. 1007 (In Russian).

16. F.Jacob and J.Monod. J. Mol. Biol. 1961. Vol. 3. P. 318.

17. J.von Neumann, A. W. Burks. "Theory of self-reproducing automata". Univ. of Illinois Press, Urbana IL, 1966.

# Quasispecies

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: QUASIS.html

Quasispecies is a model of informational sequences evolution [1,2]. The evolved population is a set {Sk} of n sequences, k = 1,..., n. Each sequence is a string of N symbols, Ski , i = 1,..., N. The symbols are taken from an alphabet, containing l letters. For example, we can consider a two-letter alphabet (l = 2, Ski = 1, -1 or Ski = G, C) or a four-letter alphabet (l = 4, Ski = G, C, A, U). The sequence length N and the population size n are assumed to be large: N , n >> 1.

Sequences are the model "organisms", they have certain (nonnegative) selective values fk = f(Sk). We assume here, that there is the master sequence Sm , having the maximal selective value. The selective value of any sequence depends only on Hamming distance (the number of different symbols at corresponding places in sequences) between given S and master sequence Sm : f(S) = f(r(S,Sm)) - the smaller is the distance r , the greater is the selective value f . For simplicity we assume here, that values f are not greater than 1.

The evolution process consists of consequent generations. New generation {Sk (t+1)} is obtained from the old one {Sk(t)} by selection and mutations of sequences Sk (t) ; here t is the generation number. The model evolution process can be described formally in the following computer-program-like manner.
 Step 0. (Formation of an initial population {Sk (0)} ) For every k = 1 , ..., n, for every i = 1 , ..., N , choose randomly a symbol Ski by setting it to an arbitrary symbol from given alphabet. Step 1. (Selection)
 Substep 1.1. (Selection of a particular sequence). Choose randomly a sequence number k*, and select the sequence Sk*(t) (without canceling it from the old population) into the new population {Sk(t+1)} with the probability fk* = f (Sk* (t)). Substep 1.2. (Iteration of the sequences selection, control of the population size). Repeat the substep 1.1 until the number of sequences in the new population reaches the value n .
 Step 2. (Mutations) For every k = 1 , ..., n, for every i = 1 , ..., N , change with the probability P the symbol Ski(t+1) to an arbitrary other symbol of the alphabet. Step 3. (Organization of the iterative evolution). Repeat the steps 1, 2 for t = 0, 1, 2, ...

The evolution character depends strongly on the population size n. If n is very large (n >> lN ), the numbers of all sequences in a population are large and the evolution can be considered as deterministic process. In this case the population dynamics can be described in terms of the ordinary differential equations and analyzed by well known methods. The main result of such an analysis [1-4] are the following conclusions: 1) the evolution process always converges, and 2) the final population is a quasispecie, that is the distribution of the sequences in the neighborhood of the master sequence Sm.

In the opposite case (lN >> n), the evolution process is essentially stochastic, and computer simulations as well as reasonable quantitative estimations can be used to characterize the main evolution features [1,2,5]. At large sequence length N (N > 50) we have just this case for any real population size.

The main evolution features and the estimations in the stochastic case for two-letter alphabet ( l = 2; Ski = 1, -1 ) are described in the child node Estimation of the evolution rate . It is shown that the total number of generations T , needed to converge to a quasispecie at sufficiently large selection intensity, can be estimated by the value
 T ~ (N/2)x(PN)-1, (1)

where P is a mutation intensity. This estimation implies a sufficiently large population size
 n > T, (2)

at which the effect of the neutral selection [6] can be neglected (see Estimation of the evolution rate, Neutral evolution game for details).

It is interesting to estimate, how effective can be an evolution algorithm of searching. Namely, what is a minimal value of the total number of participants ntotal = nT , which are needed to find a master sequence in evolution process? According to (1) , (2) , to minimize ntotal , we should maximize the mutation intensity P . But at large P , the already found "good" sequences could be lost. "Optimal" mutation intensity P ~ N -1 corresponds approximately to one mutation in any sequence per generation. Consequently, we can conclude that an "optimal" evolution process should involve of the order of
 ntotal = nT ~ N 2 (3)

participants, to find the master sequence.

This value can be compared with the participant number in deterministic and pure random methods of search. The simple deterministic (sequential) method of search (for the considered Hamming-distance-type selective value and two-letter alphabet, Si = 1, -1 ) can be constructed as follows: 1) start with arbitrary sequence S , 2) try to change consequently its symbols: S1 --> - S1 , S2 --> - S2 , ... , by fixing only such symbol changes, those increase the sequence selective value. The total number of sequences, which should be tested in order to find the master sequence Sm in such a manner, is equal to N : ntotal = N . In a pure random search, to find Sm , we need to inspect of the order of 2N sequences : ntotal ~ 2N .

So, we have the following estimations:

 Deterministic search ntotal = N Evolutionary search ntotal ~ N 2 Random search ntotal ~ 2N

Thus, for simple assumptions (Hamming-distance-type selective value and two-letter alphabet), the evolution method of search is essentially more effective than the random one, but it is something worse as compared with the deterministic search.

The Hamming-distance-type model implies that there is unique maximum of the selective value. This is a strong restriction. Using the spin-glass concept (see Spin-glass model of evolution), it is possible to construct a similar model of informational sequences evolution for the case of very large number of the local maxima of a selective value. The evolution rate, restriction on population size, and total number of evolution participants in that model can be also roughly estimated by formulas (1) - (3). But unlike the Hamming-distance model, the spin-glass-type evolution converges to one of the local selective value maxima, which depends on a particular evolution realization.

Conclusion. Quasispecies describes quantitatively a simple information sequence evolution in terms of sequence length, population size, and mutation and selection intensities. This model can be used to characterize roughly the hypothetical prebiotic polynucleotide sequence evolution and to illustrate mathematically general features of biological evolution.

References:

1. M.Eigen. Naturwissenshaften. 1971. Vol.58. P. 465.

2. M.Eigen, P.Schuster. "The hypercycle: A principle of natural self-organization". Springer Verlag: Berlin etc. 1979.

3. C.J.Tompson, J.L.McBride. Math. Biosci. 1974. Vol.21. P.127.

4. B.L.Jones, R.H.Enns, S.S. Kangnekar. Bull. Math. Biol. 1976. Vol.38. N.1. P.15.

5. V.G.Red'ko. Biofizika. 1986. Vol. 31. N.3. P. 511. V.G.Red'ko. Biofizika. 1990. Vol. 35. N.5. P. 831 (In Russian).

6. M. Kimura. "The neutral theory of molecular evolution". Cambridge Un-ty Press. 1983.

# Estimation of the evolution rate

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: ESTEVR.html

We estimate here the evolution rate for the Quasispecies model, which describes the evolution of informational sequences population {Sk} , k = 1,..., n. The sequence symbols are taken from an alphabet, containing l letters, the sequence length equals to N . The evolution process consists of consequent generations, which include selection and mutations of the sequences Sk .

We consider the stochastic case (lN >> n) under the following simplifying assumptions:
 1) the alphabet consists of two letters (l = 2), namely, sequence symbols take the values: Ski = 1, -1; k = 1,..., n ; i = 1,..., N ; 2) the selective value of any sequence S is defined as:
 f(S) = exp[-b r(S,Sm)], (1)
 where b is a selection intensity parameter, r(S,Sm) is the Hamming distance between the given sequence S and the master sequence Sm ; 3) the mutation intensity P , that is the probability to reverse a sign of any symbol (Ski --> - Ski) during mutations, is sufficiently small:
 PN b, 1. (2)

Note, that for large P , the already found "good" sequences could be lost, so the inequality (2) is a condition for the successful evolutionary search of the sequences with large selective values (see [1,2] for details). The inequality (2) implies also a rather large selection intensity.

In addition, we assume that the population size n is sufficiently large, so a neutral selection effect [3] can be neglected (see below for the more detailed consideration).

Fig. 1 illustrates schematically the sequence distribution dynamics during the evolution process. Here n(r) is the number of sequences S , such that r(S,Sm) = r in a considered population; t is the generation number.

Fig. 1. The sequence distribution n(r) at different generations t ; t3 > t2 > t1 (schematically, according to the computer simulations [2]).

The initial sequence distribution (t = 0) is a random one, it spreads in the vicinity of the value r0 = N/2 (r0 is the mean distance between an arbitrary sequence S and the master one Sm). The sequences with small r , having large selective values, are absent in the initial population. At the first several generations, the sequences, having maximal available in initial population selective value (corresponding to the left edge of the distribution at t = 0), are quickly selected, and the distribution becomes more narrow than the initial one. Such a distribution is shown in Fig.1 by the curve at t1 .

At further generations the distribution is shifted to the left (the curves at t2 , t3) until the final distribution (placed near r = 0) is reached. Because the selection intensity b is rather large (see (2)), the "shift" process is limited mainly by mutations. Typically of the order of dt = (PN)-1 generations (dt is typical time for one mutation per sequence) are needed to shift the distribution to the left on the value dr = 1. So, we can estimate the total number of evolution generations by the value
 T ~ dt x (N/2)/dr ~ (N/2)x(PN)-1. (3)

Eq. (3) characterizes roughly the evolution rate.

So far we have neglected the neutral selection effect, which is essential at a small population size [3]. The neutral selection is the random fixation in a population an arbitrary "species", regardless of a selective value. It could suppress the search of the "good" sequences. Typical time Tn of neutral selection is of the order of a population size n (see Neutral evolution game for details). We can neglect the neutral selection, if the total generation number T is smaller than Tn :
 T < Tn ~ n . (4)

The inequality (4) is a condition, at which the estimation of the evolution rate (3) is valid.

In addition, we can construct the "optimal" evolution process, which involves the minimal total number of participants ntotal = nT under condition that master sequence is found. The "optimal" evolution corresponds to the maximal permissible mutation intensity P ~ N -1 (see (2)). At this P , according to (3), we have T ~ (N/2) . Taking into account (4), we can set n ~ 2N and obtain finally:
 ntotal = nT ~ N 2 (5)

The estimation (5) characterizes the effectiveness of the evolution process as an algorithm for search of the optimal (master) sequence.

The estimations (3), (5) were confirmed by computer simulations [2].

References:

1. M.Eigen, P.Schuster. "The hypercycle: A principle of natural self-organization". Springer Verlag: Berlin etc. 1979.

2. V.G.Red'ko. Biofizika. 1986. Vol. 31. N.3. P. 511. V.G.Red'ko. Biofizika. 1990. Vol. 35. N.5. P. 831 (In Russian).

3. M. Kimura. "The neutral theory of molecular evolution". Cambridge Un-ty Press. 1983.

# Neutral evolution game

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: NEUTEG.html

Neutral selection plays an important role in the evolution of populations, having a finite population size n [1] . To demonstrate the neutral selection features explicitly, let's consider the pure neutral evolution game, which is defined as follows:
 1. There is a population of black and white balls, the total number of the balls in a population is equal to n . 2. The evolution consists of consequent generations. Each generation consists of two steps. At the first step we duplicate each ball, conserving its color: a black ball has two black offsprings, a white ball has two white ones. At the second step we randomly remove from a population exactly half of the balls with equal probability for black and white "species", independently of their color.

We say that the population is in l -state, if the numbers of black and white balls at a considered generation are equal to l and n-l, respectively. We can characterize the evolution by the probability Plm of a transition from l -state to m -state during one generation. Using a straightforward combinatorial consideration, we can calculate the values of Plm :

The matrix Plm determines the random Markovian process, which can be considered as an example of a simple stochastic genetic process [2]. Using the general methods of analysis of such processes [2], we can deduce that:

1) the considered process always converges to one of two absorbing states, namely, to 0-state (all balls are white), either to n-state (all balls are black);

2) at large n the characteristic number of generations Tn , needed to converge to the either absorbing state, is equal to 2n :
 Tn = 2n . (2)

Thus, although this evolution is purely neutral (black and white balls have equal chances to survive), nevertheless only one species is selected. The value Tn characterizes the neutral selection rate, it is used in our estimations (Quasispecies , Estimation of the evolution rate).

References:

1. M. Kimura. "The neutral theory of molecular evolution". Cambridge Un-ty Press. 1983.

2. S. Karlin. "A first course in stochastic processes". Academic Press. New York, London, 1968.

# Spin-glass model of evolution

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: SPINGL.html

The model of evolution, discussed in Quasispecies, implies a strong assumption: the selective value is determined by the Hamming distance between the particular and unique master sequences. Only one maximum of the selective value exists. Using the physical spin-glass concept, we can construct a similar model for a very large number of the local maxima of a selective value.

D. Sherrington and S. Kirkpatrick proposed a simple spin-glass model to interpret the physical properties of the systems, consisting of randomly interacting spins [1]. This well-known model can be described shortly as follows:
 1) There is a system S of spins Si , i = 1,..., N (the number of spins N is supposed to be large, N >> 1). Spins take the values: Si = 1, -1 . 2) The exchange interactions between spins are random. The energy of the spin system is defined as:
 E (S) = - Si
 where Jij are the exchange interactions matrix elements. Jij are normally distributed random values:
 Probability_density{Jij } = (2p)-1/2 (N-1)1/2 exp [- Jij2 (N-1)2-1] . (2)

From (1), (2) one can obtain, that the mean spin-glass energy is zero (<E > = 0) , and the mean square root value of the energy variation at one-spin reversal is equal to 2:
 MSQR {dE (Si --> - Si) } = 2 . (3)

The model (1), (2) was intensively investigated. For further consideration the following spin-glass features are essential :

- the number of local energy minima M is very large: M ~ exp(aN) , a = 0,2 ; (a local energy minimum is defined as a state SL , at which any one-spin reversal increases the energy E );

- the global energy minimum E0 equals approximately: E0 = - 0,8 N .

Let's construct the spin-glass model of evolution [2]. We suppose, that an informational sequence (a genome of a model "organism") can be represented by a spin-glass system vector S . The evolved population is a set {Sk} of n sequences, k = 1,..., n. The selective values of model "organisms" Sk are defined as:
 f(Sk) = exp[- b E(Sk)] , (4)

where b is the selection intensity parameter.

The definition (4) implies that the model genome Sk consists of different elements Ski , which pairwise interact in accordance with the random interaction matrix Jij . In order to maximize the "organism" selective value (that is to minimize the energy E(S)), it is necessary to find a such combination of elements Si , that provides maximally cooperative interactions for given matrix Jij .

As in Quasispecies , we suppose, that 1) the evolution process consists of consequent generations, 2) new generations are obtained by selection (in accordance with selective values (4)) and mutations (sign reversals of sequence symbols, Ski --> - Ski , with the probability P for any symbol) of sequences Sk . The initial population is supposed to be random.

The described spin-glass model of evolution was analyzed by means of computer simulations and rough estimations [2]. The main evolution features are illustrated by Fig.1. Here n(E) is the number of sequences S , such that E(S) = E in a considered population; t is the generation number.

Fig. 1. The sequence distribution n(E) at different generations t ; t3 > t2 > t1 ; E0 and EL are global and local energy minima, respectively; the global energy minimum E0 equals approximately: E0 = - 0,8 N. Schematically, according to the computer simulations [2].

The spin-glass-type evolution is analogous to the Hamming distance case (see Quasispecies, Estimation of the evolution rate). But unlike the Hamming-distance model, the evolution converges to one of the local energy minima EL , which can be different for different evolution realizations.

Because one mutation gives the energy change dE ~ 2 (see (3)), and the typical time for one mutation per sequence dt is of the order (PN)-1, the total number of evolution generations T (at sufficiently large selection intensity b) can be estimated by the value T ~ (|E0|/dE)xdt ~ (0.8N /2)x(PN)-1. This value is close to the estimation of value T in the Hamming-distance case. So, the estimations of the evolution rate are roughly the same for both models, and we can use formulas (1)-(3) in Quasispecies to characterize the spin-glass-type evolution as well.

Analogously to the Hamming-distance case, we can consider the sequential method of energy minimization, that is the consequent changes of symbols (Si --> - Si) of one sequence and fixation only successful reversals. The sequential search needs smaller participant number than the evolution search. Nevertheless, the evolution search provides in average a more deep local energy minimum EL [2], because different valleys in energy landscape are looked through in evolution process simultaneously with descending to energy minima.

Thus, in the spin-glass case, the evolutionary search has a certain advantage with respect to the sequential search: it provides in average the greater selective value.

Conclusion. The spin-glass model of evolution refers to the "organisms", which have many randomly interacting genome elements. Evolution can be considered as a search of such genome elements, which are able to cooperate in the most successful manner.

References:

1. D.Sherrington , S.Kirkpatrick. // Physical Review Letters. 1975. V.35. N.26. P.1792. S.Kirkpatrick, D.Sherrington. // Physical Review B. 1978. V.17. N.11. P.4384.

2.V.G.Red'ko. Biofizika. 1990. Vol. 35. N.5. P. 831 (In Russian).

# Hypercycles

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: HYPERC.html

M. Eigen and P. Schuster proposed the model of hypercycles [1], as a hypothetical stage of macromolecular evolution, which could follow quasispecies.

The hypercycle is a self-reproducing macromolecular system, in which RNAs and enzymes cooperate in the following manner (Fig.1): there are RNA matrices ( Ii ); i-th RNA codes i-th enzyme Ei (i = 1,2,...,n); the enzymes cyclically increase RNA's replication rates, namely, E1 increases replication rate of I2 , E2 increases replication rate of I3 , ..., En increases replication rate of I1 . In addition, the mentioned macromolecules cooperate to provide primitive translation abilities, so the information, coded in RNA-sequences, is translated into enzymes, analogously to the usual translation processes in biological objects. The cyclic organization of the hypercycle ensures its structure stability. For effective competition, the different hypercycles should be placed in separate compartments.

Fig. 1. Hypercycle structure. Ii are RNA matrices, Ei are replication enzymes (i = 1,2,...,n).

The replication enzymes ensure the more accurate RNAs' replication as compared with quasispecies, providing opportunities for further macromolecular structure improvements. M. Eigen and P. Schuster consider hypercycles as predecessors of protocells (primitive unicellular biological organisms) [1].

Let's review shortly the mathematical description of hypercycles, taking into account the original model [1] as well as the analysis by R.Feistel, Yu. M. Romanovskii, and V.A.Vasil'ev [2], who modeled a hypercycles competition in coacervate-type compartments.

We suppose, that 1) the hypercycles are placed into coacervates [3]; 2) each coacervate includes only one type of the hypercycles; 3) any coacervate volume is proportional to the number of macromolecules inside it; 4) a translation process is much quicker than a replication one (the latter means that it is sufficient to consider the RNAs dynamics only [1]). Using these assumptions, we obtain the following equations:
 dNi /dt = Vfi , V = c-1 Si Ni , xi = Ni /V , i = 1,2,..., n, (1)

where Ni and xi are a number of molecules of i-th RNA and its concentration in a coacervate, respectively; V is a coacervate volume; c is a constant, characterizing a total concentration of macromolecules in a given coacervate; fi is a synthesis rate of i-th RNA. Values fi are defined as:
 fi = a xi xj , i = 1,2,..., n, j = i -1 + ndi1, (2)

where a is a synthesis rate parameter (for the sake of simplicity we assume the value a to be the same for all RNAs in a given hypercycle), dij is the Kronecker symbol (dij = 1, if j = i; dij = 0, otherwise). Eq. (2) takes into account the cyclic structure of hypercycles (see Fig.1). From (1) we obtain:
 dxi /dt = fi - xi c-1 Sj fj , (3)
 dV/dt = V c-1 Sj fj . (4)

According to (2),(3), the concentration dynamics in a particular hypercycle is described by the nonlinear ordinary differential equations, which were analyzed [1] by qualitative methods. The analysis showed, that if the number of hypercycle member n is less than or equal to 4, the macromolecules concentrations xi converge to the following equilibrium values:
 x0i = c /n, (5a)

and if n > 4 the concentrations xi converge to a stable limit cycle, that is a periodic orbit in the concentration hyperspace. For the limit cycles, the mean values of the macromolecules concentrations xi are determined by the same formula [1] :
 = c /n. (5b)

If several hypercycles have different replication abilities, their volumes increase with different rates. Averaging on periodic concentration oscillations (if needed), we can deduce, that according to (4), the hypercycle, having the greatest value
 Wk = < ck-1 Sj fkj > (6)

(k is the hypercycle type index), possesses the greatest selective ability.

In order to consider the hypercycles competition explicitly, let's suppose, that any coacervate splits into two parts, when its volume exceeds a certain critical value, and that the total volume of all types of hypercycles is restricted by some constant: Sk Vk = VT = const (Vk is the total volume of k-th type of hypercycles). Then instead of Eq. (4), we have:
 dVk/dt = Wk Vk - Vk [VT-1SkWkVk] . (7)

The selective values Wk can be calculated from (2), (3), (5), (6) [2]:
 Wk = ak ck /nk (8)

Eq. (7) is well known [1]; according to its solution, only the hypercycle, having the maximal selective value Wm = maxk {Wk}, survives during competition.

Conclusion. Developing the hypercycle model, M.Eigen and P.Schuster discussed the very profound and difficult problem: how could the real very complex translation mechanism and unique genetic code be created during macromolecular self-organizing process? The problem was not solved, but the plausible evolution steps were outlined and the corresponding well-defined mathematical model was developed.

References:

1. M.Eigen, P.Schuster. "The hypercycle: A principle of natural self-organization". Springer Verlag: Berlin etc. 1979.

2. R.Feistel, Yu. M. Romanovskii, and V.A.Vasil'ev. Biofizika. 1980. Vol. 25. N.5. P. 882. (In Russian).

3. A.I.Oparin. "The origin of life". New York, 1938. A.I.Oparin. "Genesis and evolutionary development of life". New York, 1968.

# Sysers

Author: V.G. Red'ko
Updated: Apr 27, 1998
Filename: SYSERS.html

The model of the syser was proposed independently by V.A. Ratner and V.V. Shamin, D.H. White, and R. Feistel [1-3]. The term SYSER is the abbreviation of SYstem of SElf-Reproduction.

A syser includes a polynucleotide matrix I, a replication enzyme E1, a translation enzyme E2 , and optional proteins E3 , E4, ..., En (Fig. 1).

Fig 1. The general scheme of a syser. I is the polynucleotide matrix, E1 and E2 are the replication and the translation enzymes, respectively, E3 , E4, ..., En are optional proteins.

The polynucleotide matrix I codes proteins, the replication enzyme E1 provides the matrix replication process, the translation enzyme E2 provides the protein synthesis according to an information, stored in the matrix I . We can imply that there is the translation enzyme system (consisting of several enzymes) rather than the single translation enzyme - such a substitution does not change the mathematical description of sysers. The same is valid for the replication enzyme.

Whereas hypercycles [4] can be treated as a plausible model of the origin of translation mechanisms, sysers are more similar to real biological organisms than hypercycles. Nevertheless, possible scenarios of an origin of simple sysers from small molecules were also discussed [1,2]. Contrary to the hypercycle, the syser has a universal RNA replication enzyme. The mathematical analysis of sysers [3,5,6] is similar to that of hypercycles.

Analogously to hypercycles, different sysers should be placed into different compartments for effective competition. For example, we can model the sysers' competition, using the following assumptions [7]: 1) the different sysers are placed into separate coacervates [8], 2) any coacervate volume grows proportionally to the total macromolecules synthesis rate, 3) any coacervate splits into two parts when its volume exceeds a certain critical value. During a competition, a syser, having a maximal total macromolecules synthesis rate, is selected [3,5].

The model of sysers provides the ability to analyze evolutionary stages from a mini-syser, which contains only matrix I and replication E1 and translation E2 enzymes, to protocell, having rather real biological features. Some features can be modeled by assigning certain functions to optional proteins (Fig.1). For example, "Adaptive syser" [6] includes a simple molecular control system, which "turns on" and "turns off" synthesis of some enzyme in response to the external medium change; the scheme of this molecular regulation is similar to the classical model by F. Jacob and J. Monod [9]. The mathematical models of the adaptive syser as well as that of the mini-syser are described in the child node Adaptive syser: the model of hypothetical prebiological control system .

The scheme of sysers is similar to that of the self-reproducing automata by J. von Neumann [10]. The self-reproducing automata components and their syser's counterparts can be represented as follows:
 Self-reproducing automata by J. von Neumann Sysers Linear storing chain L Polynucleotide matrix I Constructing automaton A for manufacturing an arbitrary automaton according to description, stored in the chain L Translation enzyme E2 Automaton B for copying of the chain L Replication enzyme E1 Automaton C, needed to control the whole reproduction and to separate of the produced "child" system from the "parent" one Splitting of a coacervate during a syser growth

Conclusion. Sysers is a rather universal model of self-reproducing system. It provides the ability to analyze evolutionary stages from a very simple macromolecular systems to protocells, having real biological features.

References:

1. V.A. Ratner and V.V. Shamin. In: Mathematical models of evolutionary genetics. Novosibirsk: ICG, 1980. P.66. V.A. Ratner and V.V. Shamin. Zhurnal Obshchei Biologii. 1983. Vol.44. N.1. PP. 51. (In Russian).

2. D.H. White. J. Mol. Evol. 1980. Vol.16. N.2. P.121.

3. R. Feistel. Studia biophysica.1983. Vol.93. N.2. P.113.

4. M. Eigen, P. Schuster. "The hypercycle: A principle of natural self-organization". Springer Verlag: Berlin etc. 1979.

5. V.G. Red'ko. Biofizika. 1986. Vol. 31. N.4. P. 701 (In Russian).

6. V.G. Red'ko. Biofizika. 1990. Vol. 35. N.6. P. 1007 (In Russian).

7. R. Feistel, Yu. M. Romanovskii, and V.A.Vasil'ev. Biofizika. 1980. Vol. 25. N.5. P. 882. (In Russian).

8. A.I. Oparin. "The origin of life". New York, 1938. A.I.Oparin. "Genesis and evolutionary development of life". New York, 1968.

9. F. Jacob and J. Monod. J. Mol. Biol. 1961. Vol. 3. P. 318.

10. J. von Neumann, A. W. Burks. "Theory of self-reproducing automata". Univ. of Illinois Press, Urbana IL, 1966.

Author: V.G. Red'ko
Updated: Apr 27, 1998

 Synopsys: Adaptive syser:>a model of a hypothetical prebiological control system

We describe here the model of an adaptive syser [1], which can modify its behavior in accordance with external environmental change. We consider also the mini-syser and compare selective abilities of both sysers.

The mini-syser (Fig. 1a) is a very simple syser, it includes only such macromolecules, which are necessary and sufficient for self-reproduction, namely, the matrix I , the replication enzyme E1 , and the translation enzyme E2 .

The adaptive syser (Fig. 1b) includes two additional enzymes: the regulatory enzyme E3 and the adapting enzyme (adapter) E4 . The regulatory enzyme E3 recognizes the environment state and "turns on" or "turns off" the synthesis of the adapter in accordance with the environment changes.

Fig 1. The schemes of mini-syser (a) and adaptive syser (b). I is the polynucleotide matrix, E1 , E2 , E3 , and E4 are replication, translation, regulatory, and adapting enzymes, respectively.

We suppose that there are two types of external environment, A and B. The environment A is a usual one, in which both sysers are able to reproduce themselves. The environment B is unusual, in which the macromolecular synthesis takes place only in the adaptive syser. The regulatory enzyme E3 in the adaptive syser is synthesized with a small rate in both A and B environments, it recognizes the environment state and "turns on" the synthesis of the adapter E4 in the environment B and "turns off" this synthesis in the environment A. The adapter E4 provides the macromolecular synthesis in the adaptive syser in the environment B.

For example, we may imply that the environment A corresponds to the usual syser's "food", usual powerful chemical substrate SA , and the environment B corresponds to the usually "uneatable" chemical substrate SB , which can be transformed into "eatable food" SA by means of the adapter E4. "For the sake of economy", it is natural to synthesize the adapter only then, when it is really needed, i.e. only in the environment B. To recognize the environment state, the regulatory enzyme E3, which is synthesized always, but "economically" (with a small rate), is included in the syser structure. The scheme of this molecular control is similar to the classical model by F. Jacob and J. Monod [2].

To describe quantitatively the syser's features, we use the following assumptions: 1) the different sysers are placed into separate coacervates; 2) any coacervate volume is proportional to the number of macromolecules inside it. From these assumptions we obtain the following equations:
 dNi /dt = Vfi , V= c-1Si Ni , xi = Ni /V , (1)

where Ni and xi are the number of molecules and the concentration of i-th macromolecules in a given coacervate, respectively; V is coacervate volume; fi is the synthesis rate i-th macromolecules; c is the constant total concentration of macromolecules in a coacervate (c = Si xi = const); here the index i = 0 refers to matrix I, and other i (= 1,2,3,4) refer i-th enzymes (E1 , E2 , E3 , E4), respectively. We define the synthesis rates as follows.

For the mini-syser we set:
 f0 = a0 x0 x1 , fi = ai x0 x2 , i = 1,2, in environment A , (2a)
 f0 = f1= f2 = 0 , in environment B . (2b)

For the adaptive syser we set:
 f0 = a0 x0 x1 , fi = ai x0 x2 , i = 1,2,3, in environment A , (3a)
 f0 = b0 x0 x1 , fi = bi x0 x2 , i = 1,2,3,4, in environment B . (3b)

Here ai and bi are synthesis rate parameters. Eqs. (2), (3) state that the matrix/enzyme synthesis rate is proportional to the matrix and replication/translation enzyme concentrations.

From (1) we obtain:
 dxi /dt = fi - xi c-1 Sj fj , (4)
 dV/dt = V c-1 Sj fj . (5)

According to (2) - (4), the concentration dynamics in a particular syser is described by nonlinear ordinary differential equations, which were analyzed [1] by usual qualitative methods. The analysis showed that the macromolecules concentrations xi converge to the equilibrium stable state x0 = {x0i }. In the environment A the values x0i are expressed as (for both sysers):
 x00 = c a0 a1 D , x0i = c ai a2 D , D = [a0a1 + a2 (a1+ ... + an)]-1, i = 1, ..., n, (6)

where n is a number of enzymes in a considered syser (here we set a4 = 0) . For the adaptive syser in the environment B, the equilibrium concentrations x0i are also determined by Eq. (6) after substitution bi instead ai .

According to equation (5), the coacervate volume rates are proportional to selective values:
 W = c-1 Sj fj . (7)

Analogously to hypercycles, we can consider the competition of mini-syser and adaptive syser explicitly, supposing that the total coacervates volume for both types of sysers should be constant. During competition, a syser, having the maximal selective value W , is selected. Assuming small convergence time to the equilibrium state x0 and substituting values x0i , determined by formulas (6), into (7) , we obtain the selective values of the considered sysers. For the mini-syser we have
 WMini_A = c a0 a1 a2 [a0a1 + a2 (a1+ a2 )]-1 , WMini_B = 0, (8)

in the environments A and B, respectively. For the adaptive syser the corresponding selective values are expressed as follows:
 WAdaptive_A = c a0 a1 a2 [a0 a1+ a2 (a1+ a2 + a3)]-1 , WAdaptive_B = c b0 b1 b2 [b0 b1 + b2 (b1+ b2 + b3 + b4)]-1 . (9)

These expressions show, that in the environment A , the mini-syser has a selective advantage with respect to the adaptive one, because WMini_A is always greater, than WAdaptive_A . Such a disadvantage of the adaptive syser is due to the necessity to synthesize always the additional regulatory enzyme E3 . The disadvantage is small, if regulatory enzyme synthesis rate is small (a3 << a1, a2). Obviously, the adaptive syser is preferable in the environment B.

If the environment states (A and B) are intermittent, we can introduce the effective selective values of the considered sysers:
 WMini = (1 - PB) WMini_A , (10)

where PB is the probability of the environment B . The adaptive syser has a selective advantage with respect to the mini-syser, if WAdaptive > WMini . From expressions (8)-(11) we can see that the adaptive syser is significantly preferable, if the regulatory enzyme synthesis rate is small (a3 << a1, a2) and the macromolecular synthesis rate in the environment B as well as the probability of this environment are sufficiently large (bi ~ ai and PB ~ 1).

Thus, the adaptive syser does have a selective advantage with respect to the mini-syser, however not always, but only if the "expenses", needed to support the molecular control system operation, are sufficiently small.

Conclusion. The control system of the adaptive syser could be the first control system, which was "invented" by biological evolution. The adaptive syser model demonstrates quantitatively, that the new evolutionary invention has selective advantages, if "invention profits" are greater than "invention expenses".

References:

1. V.G.Red'ko. Biofizika. 1990. Vol. 35. N.6. P. 1007 (In Russian).

2. F.Jacob and J.Monod. J. Mol. Biol. 1961. Vol. 3. P. 318.

# General Models of Evolution

Author: V.G. Red'ko
Updated: Sep 9, 1998
Filename: GENMODEV.html

## Mathematical models of population genetics.

The mathematical theory of population genetics was grounded by R.A.Fisher, J.B.S. Haldane, and S.Wright in the 1910-1930s. Population genetics or the synthetic theory of evolution is based on the Darwinian concept of natural selection and Mendelian genetics. Numerous experiments on a small fruit fly, Drosophila, played an important role in finding an agreement between the Darwinian assumption of gradual, continuous evolutionary improvements and the discrete character of evolution in Mendelian genetics. According to population genetics, the main mechanism of progressive evolution is the selection of organisms with advantageous mutations.

The mathematical theory of population genetics describes quantitatively the gene distribution dynamics in evolving populations. The theory includes two main types of models: deterministic models (implying an infinitely large population size) and stochastic ones (finite population size). See Mathematical methods of population genetics for details.

Population genetics was intensively developed until the 1960s, when the difficulties of genetics became clear from molecular investigations.

## The neutral theory of molecular evolution.

The revolution in molecular genetics occurred in the 1950-1960s. The structure of DNA was established (F.H.C.Crick, J.D.Watson, 1953), the scheme of protein synthesis (according to information coded in DNA) became known, and the genetic code was deciphered.

As to the evolution aspects, the evolutionary rate of amino-acids substitutions as well as the protein polymorphism were estimated. In order to explain these experimental results, Motoo Kimura proposed the neutral theory of molecular evolution [1,2]. The main assumption of Kimura's theory is: the mutations at the molecular level (amino- and nuclear-acid substitutions) are mostly neutral or slightly disadvantageous (essentially disadvantageous mutations are also possible, but they are eliminated effectively from populations by selection). This assumption agrees with the mutational molecular substitution rate observed experimentally and with the fact that the rate of the substitutions for the less biologically important part of macromolecules is greater than for the active macromolecule centers.

Using mathematical methods of population genetics, M. Kimura deduced a lot of the neutral theory consequences, which are in rather good agreement with molecular genetics data [2].

The mathematical models of the neutral theory are essentially stochastic, that is, a relatively small population size plays an important role in the fixation of the neutral mutations.

If molecular substitutions are neutral, then why is progressive evolution possible? To answer this question, M.Kimura uses the concept of gene duplication developed by S.Ohno [3]. According to M.Kimura, gene duplications create unnecessary, surplus DNA sequences, which in turn drift further because of random mutations, providing the raw material for a creation of new, biologically significant genes.

The evolutionary concepts of the neutral theory came from interpretations of biological experiments; this theory was strongly empirically inspired. The other type of theory, a more abstract one, was proposed by Stuart A. Kauffman: NK automata or Boolean networks.

Theoretical population genetics and the neutral theory of molecular evolution describe the general features of genetic evolution. Nevertheless, these theories don’t consider the cybernetic properties of biological organisms. The theory of NK automata by S.A.Kauffman is a very interesting step towards understanding the evolution of the "program-like, computational" abilities of biological systems. This theory is mainly illustrative, however, it provides "a challenging scenario" (well developed mathematically) of the cybernetic evolution of the living cells.

References:

1. M. Kimura. Nature. London, 1968.V.217. PP.624.

2. M. Kimura. "The neutral theory of molecular evolution". Cambridge Un-ty Press. 1983.

3. S. Ohno. "Evolution by gene duplication". Berlin, Springer-Verlag, 1970.

# Mathematical Methods of Population Genetics

Author: V.G. Red'ko
Updated: Sep 9, 1998
Filename: MATHMPG.html

The mathematical methods of population genetics theory characterize quantitatively the gene distribution dynamics in evolving populations [1-3]. There are two types of models: deterministic and stochastic. Deterministic models are based on the approximation of an infinitely large population size. In this case the fluctuations of gene frequencies (in a gene distribution) can be neglected and the population dynamics can be described in terms of the mean gene frequencies. The stochastic models describe the probabilistic processes in finite size populations. Here we review very briefly the main equations and mathematical methods of population genetics by considering the most representative examples.

## Deterministic models

Let's consider a population of diploid1) organisms with several alleles2) A1 , A2 ,..., AK in some locus3). We assume that the organism fitness is determined mainly by the considered locus. Designating the number of organisms and the fitness of the gene pair Ai Aj by nij and Wij, respectively, we can introduce the genotype and gene frequencies Pij and Pi , as well as the mean gene fitnesses Wi in accordance with the expressions [1,2,4]:

 Pij = nij /n , Pi = S j Pij , Wi =Pi-1 S j Wij Pij , (1)

where n is the population size, index i refers to the class of organisms {Ai Aj}j=1,2,..., K , which contain the gene Ai. The population is supposed to be a panmictic4) one: during reproduction the new gene combinations are chosen randomly throughout in the whole population. For panmictic populations the Hardy-Weinberg principle can be approximately applied [1]:

 Pij =Pi Pj , i, j = 1,...,K . (2)

Eqs. (2) implies, that during mating the genotypes are formed proportionally to the corresponding gene frequencies.

The evolutionary dynamics of the population in terms of the gene frequencies Pi can be described by the following differential equations [1,2,4]:

 dPi /dt = Wi Pi - Pi - S j uji Pi + S j uij Pj , i = 1,...,K , (3)

where t is time, <W> = S ij Wij Pij is the mean fitness in a population; uij is the mutation rate of the transition Aj --> Ai, uii =0 (i, j = 1,..., K). The first term in the right side of Eqs. (3) characterizes the selection of the organisms in accordance with their fitnesses, the second term takes into account the condition S i Pi = 1, the third and fourth terms describe the mutation transitions.

Note that similar equations are used in the quasispecies model (for the deterministic case) [5].

Neglecting the mutations, we can analyze the dynamics of genes in the population by means of the equations:

 dPi /dt = Wi Pi - Pi , i = 1,...,K . (4)

Using (1), (2), (4), one can deduce (under the condition that the values Wij are constant), that the rate of increase for the mean fitness is proportional to the fitness variance V = S i Pi ( Wi - <W>)2 [1,3]:

 d/dt = 2 S i Pi ( Wi - )2 . (5)

In accordance with (4), (5), the mean fitness <W> always increases, until an equilibrium state (dPi /dt = 0) is reached.

The equation (5) characterizes quantitatively The Fundamental Theorem of Natural Selection (R.A.Fisher, 1930), which in our case can be formulated as follows [3]:

In a sufficiently large panmictic population, where the organisms' fitness is determined by one locus and the selection pressure parameters are defined by the constant values Wij, the mean fitness in a population increases, reaching a stationary value in some genetic equilibrium state. The increase rate of the mean fitness is proportional to the fitness variance; it becomes zero in an equilibrium state.

The described model is a simple example of the deterministic approach. The wide spectrum of analogous models, which describe the different particularities, concerning several gene loci, age and female/male distributions in a population, inbreeding, migrations, subdivisions of populations, were developed and investigated, especially in connection with concrete genetic data interpretations [1,3,4].

## Stochastic models

Deterministic models provide effective methods for evolving population description. However, they use the approximation of an infinitely large population size, which is too strong for many real cases. To overcome this limitation, the probabilistic methods of population genetics were developed [1,3,4,6]. These methods include the analysis by means of Markov chains (especially, by using the generating functions) [4,7], and the diffuse approximation [1,3,4,6].

Below we sketch the main equations and some examples of the diffuse approximation. This approximation provides a non-trivial and effective method of population genetics.

We consider a population of diploid organisms with two alleles A1 and A2 in a certain locus. The population size n is supposed to be finite, but sufficiently large, so that the gene frequencies can be described by continuous values. We also suppose that the population size n is constant.

Let's introduce the function j = j (X,t|P,0) , which characterizes the probability density of the frequency X of the gene A1 at the time moment t under condition that the initial frequency (at t = 0) of this gene is equal to P. Under the assumption that the changes of the gene frequencies at one generation are small, the populations dynamics can be described approximately by the following partial differential equations [1,3,4]:

 ¶j/¶t = - ¶ (Md X j )/¶X + (1/2) ¶ 2(Vd X j )/¶X2 , (6)
 ¶j/¶t = Md P ¶j/¶P + (1/2)Vd P ¶ 2j/¶P2 , (7)

where Md X , Md P and Vd X , Vd P are the mean values and the variances of the changes of the frequencies X, P during one generation; time unit is equal to one generation. Eq. (6) is the forward Kolmogorov differential equation (in physics it is called the Fokker-Planck equation); Eq. (7) is the backward Kolmogorov differential equation.

The first terms in the right sides of Eqs. (6), (7) describe a systematic selection pressure, which is due to the fitness difference of the genes A1 and A2. The second terms characterize the random drift of the frequencies, which is due to the fluctuations in the finite size population.

Using Eq. (6), one can determine the time evolution of the gene frequency distribution, Eq. (7) provides the means to estimate the probabilities of gene fixation.

Assuming that 1) the fitnesses of gene A1 and A2 are equal to 1 and 1-s, respectively and 2) the gene contributions to the fitnesses of the gene pairs A1 A1, A1 A2, and A2 A2 are additive, one can obtain, that the values Md X , Md P and Vd X , Vd P are determined by the following expressions [1,3,4]:

 Md X = sX(1-X) , Md P = sP(1-P) , Vd X = X(1-X)/2n , Vd P = P(1-P)/2n . (8)

If the evolution is purely neutral (s = 0), Eq. (6) takes the form:

 ¶j/¶t = (1/4n) ¶ 2[X(1-X)j]/¶X2 . (9)

This equation was solved analytically by M.Kimura [1,6]. The solution is rather complex. The main results can be summarized as follows: 1) only one gene (A1 or A2) is fixed in the final population, 2) the typical transition time from the initial gene frequency distribution to the final one is of the order of 2n generations. Note that these results agree with the results of a simple neutral evolution game.

Using Eq. (7), we can estimate the probability of the fixation of the gene A1 in the final population u(P). Considering the infinite time asymptotic, for the final population we can set ¶j/t = 0. The probability to be found can be approximated by the value [1]: u(P) = j/2n (here u(P) = j dX, where dX = 1/2n is the minimal frequency change step in population, see also [3] for more rigorous consideration). Using this approximation and combining (7), (8), we obtain:

 s du /dP + (1/4n) d2u /dP2 = 0 . (10)

Solving this simple equation for the natural boundary conditions: u (1) = 1, u (0) = 0, we obtain the probability of gene A1 fixation in a final population [1,3,6]:

 u(P) = [1 - exp (- 4nsP)] [1 - exp (- 4ns)]-1 . (11)

This expression shows, that if 4ns << 1, the neutral gene fixation takes place: u(P) » P, if 4ns >> 1, the advantageous gene A1 is selected: u(P) » 1; the population size nc ~ (4s)-1 is the boundary value, demarcating "neutral" and "selective" regions.

## Conclusion

The mathematical models of population genetics describe the gene frequency distributions in evolving populations. The deterministic methods are used to analyze the mean frequency dynamics; the stochastic methods take into account the fluctuations, which are due to the finite population size.

Glossary:

1) Diploid organism: An individual having two chromosome sets in each of its cells.

2) Allele: One of the different forms of a gene that can exist at a single locus.

3) Gene locus: The specific place on a chromosome where a gene is located.

4) Panmictic population: Random-mating population.

References:

1. J.F. Crow, M. Kimura. "An introduction to population genetics theory". New York etc, Harper & Row. 1970.

2. T. Nagylaki. "Introduction to theoretical population genetics ". Berlin etc, Springer Verlag. 1992.

3. Yu.M. Svirezhev, V.P. Pasekov. "Fundamentals of mathematical evolutionary genetics". Moscow, Nauka. 1982 (In Russian), Dordrecht, Kluwer Academic Publishers, 1990.

4. P.A.P. Moran. "The statistical processes of evolutionary theory", Oxford, Clarendon Press, 1962.

5. M. Eigen. Naturwissenshaften. 1971. Vol.58. P. 465. M. Eigen, P. Schuster. "The hypercycle: A principle of natural self-organization". Berlin etc, Springer Verlag. 1979.

6. M. Kimura. "The neutral theory of molecular evolution". Cambridge Un-ty Press. 1983.

7. S. Karlin. "A first course in stochastic processes". New York, London, Academic Press. 1968.

# Kauffman's NK Boolean networks

Author: V.G. Red'ko
Updated: Sep 9, 1998
Filename: BOOLNETW.html

An NK automaton is an autonomous random network of N Boolean logic elements. Each element has K inputs and one output. The signals at inputs and outputs take binary (0 or 1) values. The Boolean elements of the network and the connections between elements are chosen in a random manner. There are no external inputs to the network. The number of elements N is assumed to be large.

An automaton operates in discrete time. The set of the output signals of the Boolean elements at a given moment of time characterizes a current state of an automaton. During an automaton operation, the sequence of states converges to a cyclic attractor. The states of an attractor can be considered as a "program" of an automaton operation. The number of attractors M and the typical attractor length L are important characteristics of NK automata.

The automaton behavior depends essentially on the connection degree K.

If K is large (K = N), the behavior is essentially stochastic. The successive states are random with respect to the preceding ones. The "programs" are very sensitive to minimal disturbances (a minimal disturbance is a change of an output of a particular element during an automaton operation) and to mutations (changes in Boolean element types and in network connections). The attractor lengths L are very large: L ~ 2N/2 . The number of attractors M is of the order of N. If the connection degree K is decreased, this stochastic type of behavior is still observed, until K ~ 2.

At K ~ 2 the network behavior changes drastically. The sensitivity to minimal disturbances is small. The mutations create typically only slight variations an automaton dynamics. Only some rare mutations evoke the radical, cascading changes in the automata "programs". The attractor length L and the number of attractors M are of the order of N1/2. This is the behavior at the edge of chaos, at the borderland between chaos and order.

The NK automata can be considered as a model of regulatory genetic systems of living cells. Indeed, if we consider any protein synthesis (gene expression) as regulated by other proteins, we can approximate a regulatory scheme of a particular gene expression by a Boolean element, so that a complete network of molecular-genetic regulations of a cell can be represented as a network of a NK automaton.

S.A.Kauffman argues that the case K ~ 2 is just appropriate to model the regulatory genetic systems of biological cellular organisms, especially in evolutionary context. The main points of this argumentation are as follows:

• the regulatory genetic systems at the edge of chaos ensure both necessary stability and potential for progressive evolutionary improvements;

• typical cellular gene regulation schemes include only a small number of inputs from other genes in accordance with the value K ~ 2;

• if we compare the number of automaton attractors M at K = 2 (calculated for given number of genes N) with the number of different kind of cells ncells in biological organisms at various evolutionary levels, we find similar values; for example, for a human we have (N ~ 105): M = 370, ncells = 254 [4].

Because the regulatory structures at the edge of chaos (K ~ 2) ensure both stability and evolutionary improvements, they could provide the background conditions for an evolution of genetic cybernetic systems. That is, such systems have "the ability to evolve". So, it seems quite plausible, that such kind of regulatory genetic structures was selected at early stages of life, and this in turn made possible the further progressive evolution.

References

S. A. Kauffman. Scientific American. 1991. August. P. 64.

S. A. Kauffman. The Origins of Order: Self-Organization and Selection in Evolution, Oxford University Press, New York, 1993.

S. A. Kauffman.At Home in the Universe: The Search for Laws of Self-Organization and Complexity, Oxford University Press, Oxford, 1995.

# Artificial Evolution Models

Author: V.G. Red'ko
Filename: ARTIF.html

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# Blind Variation and Selective Retention

Author: F. Heylighen
Updated: Oct 15, 1993
Filename: BVSR.html

"Blind Variation and Selective Retention" (BVSR) is a phrase introduced by Donald T. Campbell, as a way of describing the most fundamental principle underlying Darwinian evolution. (Campbell only applied it to the evolution of knowledge, but we here apply it in the most general context). The BVSR formula can be understood as a summary of three independent principles: blind variation, asymmetric transitions, and selective retention. The second principle is implicit in the "and" of "blind-variation-and-selective-retention", since it ensures that configurations produced by blind variation can make the transition to stability, i.e. selective retention. That this is not obvious is shown by classical mechanics where unstable or variable configurations necessarily remain unstable (see asymmetric transitions) .

# The Principle of Selective Retention

Author: F. Heylighen,
Updated: Nov 1991
Filename: SELRET.html

 Synopsys: Stable configurations are retained, unstable ones are eliminated

This principle is tautological in the sense that stability can be defined as that what does not (easily) change or disappear. Instability then is, by negation, that what tends to vanish or to be replaced by some other configuration, stable or unstable. The word "configuration" denotes any phenomenon that can be distinguished. It includes everything that is called feature, property, state, pattern, structure or system.

The principle can be interpreted as stating a basic distinction between stable configurations and configurations undergoing variation. This distinction has a role in evolution which is as fundamental as that between A and not A in logic. Without negation, we cannot have a system of logic. Without (in)stability we cannot describe evolution. The tautology plays a role similar to the principle of contradiction: "A and not A cannot both be true". The distinction between stable and changing is not as absolute as that between A and not A, though. We do not require a principle of the excluded middle, since it is clear that most configurations are neither absolutely stable nor absolutely unstable, but more or less stable. In this more general formulation, the principle would read:

More stable configurations are less easily eliminated than less stable ones

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# The Principle of Asymmetric Transitions

Author: F. Heylighen,
Updated: Nov 1991
Filename: ASYMTRANS.html

 Synopsys: A transition from an unstable configuration to a stable one is possible, but the converse is not

This principle implies a fundamental asymmetry in evolution: one direction of change (from unstable to stable) is more likely than the opposite direction. The generalized, "continuous" version of the principle is the following:

The probability of transition from a less stable configuration A to a more stable one B is larger than the probability for the inverse transition: P (A -> B) > P (B -> A) (under the condition P (A -> B) =/ 0)

A similar principle was proposed by Ashby in his Principles of the Self-Organizing System (1962):"We start with the fact that systems in general go to equilibrium. Now most of a system's states are non-equilibrial [...] So in going from any state to one of the equilibria, the system is going from a larger number of states to a smaller. In this way, it is performing a selection, in the purely objective sense that it rejects some states, by leaving them, and retains some other state, by sticking to it. "

This reduction in the number of reachable states signifies that the variety, and hence the statistical entropy, of the system diminishes. It is because of this increase in neguentropy or organization that Ashby calls the process self-organization. But how does this fit in with the 2nd law of thermodynamics, which states that entropy in closed systems cannot decrease? The easy way out is to conclude that such a self-organizing system cannot be closed, and must lose entropy to its environment (von Foerster, 1960).

A deeper understanding can be reached by going back from the statistical definition of entropy to the thermodynamic one, in terms of energy or heat. Energy is defined as the capacity to do work, and working means making changes, that is to say exerting variation. Hence energy can ve viewed as potential variation. A stable configuration does not undergo variation. In order to destroy a stable equilibrium, you need to add energy, and the more stable the configuration, the more energy you will need. Therefore stability is traditionally equated with minimal energy.

The 1st law of thermodynamics states that energy is conserved. A naive interpretation of that law would conclude that the principle of asymmetric transitions cannot be valid, since it postulates a transition from an unstable (high energy) to a stable (low energy) configuration. If energy is absolutely conserved, then an unstable configuration can only be followed by another unstable configuration. This is the picture used in classical mechanics, where evolution is reversible, that is to say symmetric. Incidentally, this shows that the principle of asymmetric transitions is not tautological - though it may appear self-evident - , since a perfectly consistent theory (classical mechanics) can be built on its negation.

Thermodynamics has enlarged that picture by allowing energy dissipation. But what happens with the "dissipated" energy? A simple model is provided by a quantum system (e.g. an electron bound in an atom) with its set of - usually discrete - energy levels. A configuration at a higher level will spontaneously fall down to a lower level, emitting a photon which carries the surplus energy away. In order to bring back the electron to its higher level, energy must be added by having a photon of the right energy and direction hit the electron, a rather improbable event. Hence, the low level can be viewed as a stable configuration, with a small probability of transition.

The conjunction of energy conservation and asymmetric transitions implies that configurations will tend to dissipate energy (or heat) in order to move to a more stable state. For a closed system, this is equivalent to the thermodynamical interpretation of the 2nd law, but not to the statistical one, as the statistical entropy can decrease when transition probabilities are asymmetric. In an open system, on the other hand, where new energy is continuously added, the configuration will not be able to reach the minimum energy level. In that case we might assume that it will merely tend to maximally dissipate incoming energy, since transitions where energy is emitted are (much) more probable than transitions where energy is absorbed. That hypothesis seems equivalent to the Law of maximum entropy production (Swenson, 19), which describes dissipative structures and other far-from-equilibrium configurations. In such configurations the stability is dynamic, in the sense that what is maintained is not a static state but an invariant process.

Such an application of the principle of asymmetric transitions is opposite to the most common interpretation of the 2nd law, namely that disorder and with it homogeneity tend to increase. In the present view, configurations tend to become more and more stable, emitting energy in the process. This might be seen as a growing differentiation between the negative energy of stable bonds, and the positive energy of photons and movement. Recent cosmological theories hypothesize a similar spontaneous separation of negative and positive energies to account for the creation of the universe out of a zero-energy vacuum (Hawking, 1988).

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# Asymmetric Transitions: an illustration

Author: F. Heylighen,
Updated: Dec 19, 1994
Filename: ASYMILL.html

The essence of the principle of asymmetric transitions is that the probability of transition from state A to state B is in general different from the transition probability of B to A. This leads to evolution with a preferred direction: A -> B is more likely than B -> A. It is not necessary to imagine complex living systems or esoteric far-from-equilibrium thermodynamic set-ups to illustrate this idea. The most obvious illustration is the behavior of heavy objects that are subject to gravity and friction. If you drop a stone or ball down a slope it will normally move downwards until it finds a stable position, and not move upwards anymore. (in the unrealistic case where there is no friction, the ball will bounce back, and continue to go up and down like a jojo, until the end of time).

One might object that this is not a good illustration of the process of evolution since there is an external force, gravity, which pulls the system in a determined direction, unlike most other evolutionary systems which undergo blind variation without any sense of direction. However, it is easy to devise systems that move in a specific direction because of asymmetric transitions, independently of, or even in spite of, forces pushing them in a different direction.

Imagine the following set-up (see fig.), where a ball is positioned on a surface that looks like a slightly skewed horizontal staircase. The vertical wall of the stair makes it impossible for the ball to move to the left. However, the ball needs just a small impulse to move over the gently inclining slope on the right and reach the next stair (B). In that next state, again moving back to the left is precluded, while continuing movement to the right (C) is relatively easy. Thus we have a system where P(A->B) >> P(B->A). We can imagine that if the whole set-up is gently shaken (in random directions, i.e. blind variation), the ball will move more and more to the right and never come back.

We could even imagine that the set-up as a whole is not horizontal but slightly inclined so that position B is elevated with respect to A, and C with respect to B, etc. In that case, the random moves would make the marble effectively move upwards, against the force of gravity. We could also imagine a "staircase" forming a loop so that position Z is identical to position A (as in a famous Escher drawing where figures are climbing up a staircase that turns back into itself). In that case, the marble will just continuously cycle along the staircase, like in a 1 dimensional attractor. With a two dimensional design, much more complex figures are possible, including fractal attractors.

An alternative set-up is one where it is not the environment (the staircase) that has an asymmetrical effect, but the system (marble) itself. For example, some seeds are shaped such that they can easily enter a tissue (or animal fur), but it is almost impossible to pull them back (see Fig.). The only way to get rid of them is by pulling them forward in the direction they originally entered.

# Definition of Fitness in terms of transition probabilities

Author: Heylighen,
Updated: Dec 19, 1994
Filename: FITTRANS.html

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"Fitness", the hypothesized quality distinguishing systems or states that are selected from those that are eliminated, can be reformulated as a general measure F for the likeliness that a particular configuration x will be reached in a stochastic process:

F (xi) = Sumj P(xj -> xi) (i,j {1,...,n}, )

where P(xj -> xi) is the probability of the transition xj -> xi

High fitness (F > 1) of a state x means that on average more transitions enter x than leave x (the sum of all transition probabilities leaving x can never be >1). Thus, while the process runs, high fitness states will become more probable, and low fitness states less probable, as would be expected from a process undergoing natural selection.

A doubly stochastic process is defined by the requirement that F (xi) = 1 for all states xi. It can be shown that double stochasticity is a necessary and sufficient condition for a Markov process to be characterized by a non-decreasing statistical entropy function, i.e. a function of the probabilities which can only increase (or remain the same) over time, whatever the initial distribution of probabilities. Such processes correspond to the irreversible processes implied by the second law of thermodynamics. In a more general process, fitness can be larger or smaller for different states, and thus probability would tend to concentrate in the high fitness states, decreasing overall entropy. So, a necessary and sufficient condition for a Markov process to allow self-organization (i.e. decrease of statistical entropy) is that it have a non-trivial fitness function, i.e. there are x's such that F(x) =/ 1. Assuming classical probabilities, the sum of all P's must be 1 and thus we have the condition: Sumi F(xi) = n. Therefore, for every F(x) > 1, there must be at least one F(y) <1. This condition is equivalent to saying that selection is possible, because there are different fitness configurations from which to choose.

Having symmetric transition probabilities is a sufficient condition for a Markov process to be doubly stochastic. Indeed, symmetry means that P(xj -> xi) = P(xi -> xj) for all i,j and thus: 1 = Sumj P(xi -> xj) = Sumj P(xj -> xi) = F(xi).

Therefore, having asymmetric transition probabilities is a necessary condition for a Markov process to have non-trivial fitness, and thus, to allow self-organization.

We can also interpret the P's not as probabilities but as effective numbers or frequencies N (e.g. size of a population) which can be different from 1. In that case, the total number N = Sumi N(xi) can increase or decrease during the process, and we get a model that resembles population dynamics, where fitness gets its traditional meaning from biological evolution theory: F (x) = N(x(t)) / N(x(t-1)).

# The Principle of Blind Variation

Author: F. Heylighen,
Updated: Nov 1991
Filename: BLINDVAR.html

 Synopsys: At the most fundamental level variation processes "do not know" which of the variants they produce will turn out be be selected

This principle is not self-evident, but can be motivated by Ockham's razor. If it were not valid, we would have to introduce some explanation (e.g. design by God) to account for the "foreknowledge" of variation, and that would make the model more complicated than it needs to be. The blindness of variation is obvious in biological evolution, based on random mutations and recombinations. Yet even perfectly deterministic dynamical systems can be called blind, in the sense that if the system is complex enough it is impossible to predict whether the system will reach a particular attractor (select a stable configuration of states) without explicitly tracing its sequence of state transitions (variation) (Heylighen, 1991).

Of course many interactions are not blind. If I tackle a practical problem, I normally do not try out things at random, but rather have some expectations of what will work and what will not. Yet this knowledge itself was the result of previous trial-and-error processes, where the experience of success and failure was selectively retained in my memory, available for guiding later activities. Similarly, all knowledge can be reduced to inductive achievements based on blind-variation-and-selective-retention (BVSR) at an earlier stage. Together with Campbell (1974), I postulate that it must be possible to explain all cases of "non-blindness" (that is to say variation constrained in such a way as to make it more likely to satisfy selection) as the result of previous BVSR processes.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# The Principle of Selective Variety

Author: F. Heylighen,
Updated: Nov 1991
Filename: SELVAR.html

 Synopsys: The larger the variety of configurations a system undergoes, the larger the probability that at least one of these configurations will be selectively retained

Although this principle is again self-evident or tautologous, it leads to a number of useful and far from trivial conclusions. For example, the less numerous or the farther apart potential stable configurations are, the more variation (passing through a variety of configurations) the system will have to undergo in order to maintain its chances to find a stable configuration. In cases where selection criteria, determining which configurations are stable and which are not, can change, it is better to dispose of a large variety of possible configurations. If under a new selective regime configurations lose their stability, a large initial variety will make it probable that at least some configurations will retain their stability. A classic example is the danger of monoculture with genetically similar or identical plants: a single disease or parasite invasion can be sufficient to destroy all crops. If there is variety, on the other hand, there will always be some crops that survive the invasion.

Another special case is the "order from noise" principle (von Foerster, 1960), related to "order out of chaos". Noise or chaos can here be interpreted as rapid and blind variation. The principle states that addition of such noise makes it more likely for a system to evolve to an ordered (stable) configuration. A practical application is the technique of (simulated) annealing, where noise or variation is applied in stepwise decreasing amounts, in order to reach a maximally stable configuration.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# The Principle of Recursive Systems Construction

Author: F. Heylighen,
Updated: Nov 1991
Filename: RECSYSCO.html

 Synopsys: BVSR processes recursively construct stable systems by the recombination of stable building blocks

The stable configurations resulting from BVSR processes can be seen as primitive elements: their stability distinguishes them from their variable background, and this distinction, defining a "boundary", is itself stable. The relations between these elements, extending outside the boundaries, will initially still undergo variation. A change of these relations can be interpreted as a recombination of the elements. Of all the different combinations of elements, some will be more stable, and hence will be selectively retained.

Such a higher-order configuration might now be called a system. The lower-level elements in this process play the role of building blocks: their stability provides the firmness needed to support the construction, while their variable connections allow several configurations to be tried out. The principle of "the whole is more than the sum of its parts" is implied by this systemic construction principle, since the system in the present conception is more than a mere configuration of parts, it is a stable configuration, and this entails a number of emergent constraints and properties (Heylighen, 1991). A stable system can now again function as a building block, and combine with other building blocks to a form an assembly of an even higher order, in a recursive way.

Simon (1962) has argued in his famous "The Architecture of Complexity" that such stable assemblies will tend to contain a relatively small number of building blocks, since the larger a specific assembly, the less probable that it would arise through blind variation. This leads to a hierarchical architecture, that can be represented by a tree.

Two extensions must be made to the Simon argument (cf. Heylighen, 1989). 1) If one takes into account autocatalytic growth, as when a small stable assembly makes it easier for other building blocks to join the assembly, the number of building blocks at a given level can become unlimited. 2) It is possible, though less probable, that a given building block would participate in several, overlapping stable assemblies; it suffices that its configuration would satisfy two (or more) selection criteria, determining stable systems. It is clear, however, that the more selection criteria a configuration would have to satisfy, the less likely that such a configuration would be discovered by blind variation. These two points lead us to generalize the tree structure of Simon's "nearly-decomposable" architecture to a loose or quasi-hierarchy (Joslyn, 1991), which in parts can be very flat, and where some nodes might have more than one mother node.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# The Red Queen Principle

Author: F. Heylighen,
Updated: Dec 2, 1993
Filename: REDQUEEN.html

 Synopsys: for an evolutionary system, continuing development is needed just in order to maintain its fitness relative to the systems it is co-evolving with

This principle was proposed by the evolutionary biologist L. van Valen (1973), and is based on the observation to Alice by the Red Queen in Lewis Carroll's "Through the Looking Glass" that "in this place it takes all the running you can do, to keep in the same place."

Since every improvement in one species will lead to a selective advantage for that species, variation will normally continuously lead to increases in fitness in one species or another. However, since in general different species are coevolving, improvement in one species implies that it will get a competitive advantage on the other species, and thus be able to capture a larger share of the resources available to all. This means that fitness increase in one evolutionary system will tend to lead to fitness decrease in another system. The only way that a species involved in a competition can maintain its fitness relative to the others is by in turn improving its design.

The most obvious example of this effect are the "arms races" between predators and prey, where the only way predators can compensate for a better defense by the prey (e.g. rabbits running faster) is by developing a better offense (e.g. foxes running faster). In this case we might consider the relative improvements (running faster) to be also absolute improvements in fitness.

However, the example of trees shows that in some cases the net effect of an "arms race" may also be an absolute decrease in fitness. Trees in a forest are normally competing for access to sunlight. If one tree grows a little bit taller than its neighbours it can capture part of their sunlight. This forces the other trees in turn to grow taller, in order not to be overshadowed. The net effect is that all trees tend to become taller and taller, yet still gather on average just the same amount of sunlight, while spending much more resources in order to sustain their increased height. This is an example of the problem of suboptimization: optimizing access to sunlight for each individual tree does not lead to optimal performance for the forest as a whole.

In sum, in a competitive world, relative progress ("running") is necessary just for maintenance ("staying put").

In its emphasis on the stress that necessarily accompanies evolutionary development, the Red Queen Principle is related to the generalized "Peter Principle".

## Reference:

Van Valen L. (1973): "A New Evolutionary Law", Evolutionary Theory 1, p. 1-30.

# The generalized "Peter Principle"

Author: F. Heylighen,
Updated: Nov 30, 1993
Filename: PETERPR.html

 Synopsys: in evolution systems tend to develop up to the limit of their adaptive competence

The Peter Principle was first introduced by L. Peter in a humoristic book (of the same title) describing the pitfalls of bureaucratic organization. The original principle states that in a hierarchically structured administration, people tend to be promoted up to their "level of incompetence". The principle is based on the observation that in such an organization new employees typically start in the lower ranks, but when they prove to be competent in the task to which they are assigned, they get promoted to a higher rank. This process of climbing up the hierarchical ladder can go on indefinitely, until the employee reaches a position where he or she is no longer competent. At that moment the process typically stops, since the established rules of bureacracies make that it is very difficult to "demote" someone to a lower rank, even if that person would be much better fitted and more happy in that lower position. The net result is that most of the higher levels of a bureaucracy will be filled by incompetent people, who got there because they were quite good at doing a different (and usually, but not always, easier) task than the one they are expected to do.

The evolutionary generalization of the principle is less pessimistic in its implications, since evolution lacks the bureaucratic inertia that pushes and maintains people in an unfit position. But what will certainly remain is that systems confronted by evolutionary problems will quickly tackle the easy ones, but tend to get stuck in the difficult ones. The better (more fit, smarter, more competent, more adaptive) a system is, the more quickly it will solve all the easy problems, but the more difficult the problem will be it finally gets stuck in. Getting stuck here does not mean "being unfit", it just means having reached the limit of one's competence, and thus having great difficulty advancing further. This explains why even the most complex and adaptive species (such as ourselves, humans) are always still "struggling for survival" in their niches as energetically as are the most primitive organisms such as bacteria. If ever a species would get control over all its evolutionary problems, then the "Red Queen Principle" would make sure that new, more complex problems would arise, so that the species would continue to balance on the border of its domain of incompetence. In conclusion, the generalized Peter principle states that in evolution systems tend to develop up to the limit of their adaptive competence.

# The Growth of Complexity

Author: F. Heylighen,
Updated: Jan 18, 1995
Filename: COMPGROW.html

 Synopsys: blind variation and selective retention tend to produce increases in both structural and functional complexity of evolving systems

At least since the days of Darwin, evolution has been associated with the increase of complexity: if we go back in time we see originally only simple systems (elementary particles, atoms, molecules, unicellular organisms) while more and more complex systems appear in later stages. However, from the point of view of classical evolutionary theory there is no a priori reason why more complicated systems would be preferred by natural selection. Evolution tends to increase fitness, but fitness can be achieved as well by very complex as by very simple systems. For example, according to some theories, viruses, the simplest of living systems, are degenerated forms of what were initially much more complex organisms. Since viruses live as parasites, using the host organisms as an environment that provides all the resources they need to reproduce themselves, maintaining a metabolism and reproductory systems of their own is just a waste of resources. Eventually, natural selection will eliminate all superfluous structures, and thus partially decrease complexity.

## Complexity increase for individual (control) systems

The question of why complexity of individual systems appears to increase so strongly during evolution can be easily answered by combining the traditional cybernetic idea of the "Law of Requisite Variety" and a concept of coevolution, as used in the evolutionary "Red Queen Principle".

Ashby's Law of Requisite Variety states that in order to achieve complete control, the variety of actions a control system should be able to execute must be at least as great as the variety of environmental perturbations that need to be compensated. Evolutionary systems (organisms, societies, self-organizing processes, ...) obviously would be fitter if they would have greater control over their environments, because that would make it easier for them to survive and reproduce. Thus, evolution through natural selection would tend to increase control, and therefore internal variety. Since we may assume that the environment as a whole has always more variety than the system itself, the evolving system would never be able to achieve complete control, but it would at least be able to gather sufficient variety to more or less control its most direct neighbourhood. We might imagine a continuing process where the variety of an evolving system A slowly increases towards but never actually matches the infinite variety of the environment.

However, according to the complementary principles of selective variety and of requisite constraint, Ashby's law should be restricted in its scope: at a certain point further increases in variety diminish rather than increase the control that system A has over its environment. A will asymptotically reach a trade-off point, depending on the variety of perturbations in its environment, where requisite variety is in balance with requisite constraint. For viruses, the balance point will be characterised by a very low variety, for human beings by a very high one.

This analysis assumes that the environment is stable and a priori given. However, the environment of A itself consists of evolutionary systems (say B, C, D...), which are in general undergoing the same asymptotic increase of variety towards their trade-off points. Since B is in the environment of A, and A in the environment of B, the increase in variety in the one will create a higher need (trade-off point) in variety for the other, since it will now need to control a more complex environment. Thus, instead of an increase in complexity characterised by an asymptotic slowing down, we get a positive feedback process, where the increase in variety in one system creates a stronger need for variety increase in the other. The net result is that many evolutionary systems that are in direct interaction with each other will tend to grow more complex, and this with an ever increasing speed.

As an example, in our present society, individuals and organizations tend to gather more knowledge and more resources, increasing the range of actions they can take, since this will allow them to cope better with the possible problems appearing in their environment. However, if the people you cooperate or compete with (e.g. colleagues) become more knowledgeable and resourceful, you too will have to become more knowledgeable and resourceful in order to respond to the challenges they pose to you. The result is an ever faster race towards more knowledge and better tools, creating the "information explosion" we all know so well.

The present argument does not imply that all evolutionary systems will increase in complexity: those (like viruses, snails or mosses) that have reached a good trade-off point and are not confronted by an environment putting more complex demands on them will maintain their present level of complexity. But it suffices that some systems in the larger ecosystem are involved in the complexity race to see an overall increase of available complexity.

## Complexity increase for global (eco)systems

The resoning above explains why individual systems will on average tend to increase in complexity. However, the argument can be extended to show how complexity of the environment as a whole increases. Let us consider a global system, consisting of a multitude of co-evolving subsystems. The typical example would be an ecosystem, where the subsystems are organisms belonging to different species.

Now, it is well-documented by ecologists and evolutionary biologists that ecosystems tend to become more complex: the number of different species increases, and the number of dependencies and other linkages between species increases. This has been observed as well over the geological history of the earth, as in specific cases such as island ecologies which initially contained very few species, but where more and more species arose by immigration or by differentiation of a single species specializing on different niches (like the famous Darwin's finches on the Galapagos islands).

As is well explained by E.O. Wilson in his "The Diversity of Life", not only do ecosystems contain typically lots of niches that will eventually be filled by new species, there is a self-reinforcing tendency to create new niches. Indeed, a hypothetical new species (let's call them "bovers") occupying a hitherto empty niche, by its mere presence creates a set of new niches. Different other species can now specialize in somehow using the resources produced by that new species, e.g. as parasites that suck the bover's blood or live in its intestines, as predators that catch and eat bovers, as plants that grow on the bovers excrements, as furrowers that use abandoned bover holes, etc. etc. Each of those new species again creates new niches, that can give rise to even further species, and so on, ad infinitum. These species all depend on each other: take the bovers away and dozens of other species may go extinct.

This principle is not limited to ecosystems or biological species: if in a global system (e.g. the inside of a star, the primordial soup containing different interacting chemicals, ...) a stable system of a new type appears through evolution (e.g. a new element in a star, or new chemical compound), this will in general create a new environment or selector. This means that different variations will either be adapted to the new system (and thus be selected) or not (and thus be eliminated). Elimination of unfit systems may decrease complexity, selection of fit systems is an opportunity for increasing complexity, since it makes it possible for systems to appear which were not able to survive before. For example, the appearance of a new species creates an opportunity for the appearance of species-specific parasites or predators, but it may also cause the extinction of less fit competitors or prey.

However, in general the power for elimination of other systems will be limited in space, since the new system cannot immediately occupy all possible places where other systems exist. E.g. the appearance of a particular molecule in a pool of "primordial soup" will not affect the survival of molecules in other pools. So, though some systems in the neighbourhood of the new system may be eliminated, in general not all systems of that kind will disappear. The power for facilitating the appearance of new systems will similarly be limited to a neighbourhood, but that does not change the fact that it increases the overall variety of systems existing in the global system. The net effect is the creation of a number of new local environments or neighbourhoods containing different types of systems, while other parts of the environment stay unchanged. The environment as a whole becomes more differentiated and, hence, increases its complexity.

Heylighen F. (1996): "The Growth of Structural and Functional Complexity during Evolution", in: F. Heylighen & D. Aerts (eds.) (1996): "The Evolution of Complexity" (Kluwer, Dordrecht). (in press)

W. Brian Arthur: "Why Do Things Become More Complex?", Scientific American, May 1993

PRNCYB-L discussion on Requisite Variety, Complexity & the edge of Chaos

# The Direction of Evolution

Author: F. Heylighen,
Updated: Jun 13, 1997
Filename: DIREVOL.html

 Synopsys: although evolution is chaotic and unpredictable, it moves preferentially in the direction of increasing fitness

A fundamental criticism of the idea of increasing complexity, formulated among others by Stephen Jay Gould (1994), is that such an increase implies a preferred direction for evolution, a continuing "progress" or advance towards more sophisticated forms. Recent advances in evolutionary theory (such as the theory of punctuated equilibrium) and observation of evolutionary phenomena seem to indicate that evolution is a largely unpredictable, chaotic and contingent series of events, where small fluctuations may lead to major catastrophes that change the future course of development. At first sight, this seems inconsistent with any constant "direction". Yet, an example will show that there is no necessary contradiction.

Consider a rock that rolls down from the top of a steep mountain. Given that the slightest irregularity in the terrain may be sufficient to make the rock fall either into the one or the other of a host of downward slopes or valleys, the exact path of the rock will be virtually impossible to predict. Repeated experiments are likely to produce final resting positions that are miles apart. Yet, one thing will be certain: the final position will be lower than the initial position at the top. Although we cannot know the direction of movement in the horizontal dimensions, we do know that there is only one possible sense in which it can move along the vertical dimension: downward.

To apply this metaphor to evolution, we need to discover the equivalent of the "vertical" dimension, in other words we need to define a variable that can only increase during evolution (like vertical distance from the top). Entropy plays the role of such a variable for thermodynamic systems, but this seems hardly useful to describe complexification. Fisher's (1958) fundamental theorem of natural selection has shown that another such variable exists for populations of living systems: average fitness. This follows straightforwardly from the fact that fit individuals by definition will become more numerous, while the proportion of less fit individuals will decrease. This reasoning can be generalized to cover non-biological systems too (cf. the principle of asymmetric transitions).

It might be objected that fitness is a relative notion: what is fit in one type of environment may no longer be fit in another environment. Thus, the inexorable increase of fitness only holds in invariant environments (which seem wholly atypical if one takes into account co-evolution). Gould proposes the following example: the evolution from hairless elephant to woolly mammoth is due merely to a cooling down of the climate. If the climate becomes warmer again the woolly variant will lose its fitness relative to the hairless one, and the trend will be reversed.

Yet, there are ways to increase "absolute" fitness. First, the system may increase its internal or intrinsic fitness, by adding or strenghtening bonds or linkages between its components. This is typically accompanied by the increase of structural complexity. Second, the system may increase its fitness relative to its environment by increasing the variety of environmental perturbations that it can cope with, and thus its functional complexity.

This may be illustrated through the climate change example: though the warm-blooded, woolly mammoth is only relatively fitter than its hairless cousin, it is absolutely fitter than a cold-blooded reptile, which would never have been able to adapt to a cold climate, with or without hair. Warm-bloodedness means temperature control, i.e. the capacity to internally compensate a variety of fluctuations in outside temperature. The appearance of control is the essence of a metasystem transition, which can be seen as a discrete unit of evolutionary progress towards higher functional complexity.

All other things being equal, a system that can survive situations A, B and C, is absolutely fitter than a system that can only survive A and B. Such an increase in absolute fitness is necessarily accompanied by an increase in functional complexity. Thus, evolution will tend to irreversibly produce increases of functional complexity.

This preferred direction must not be mistaken for a preordained course that evolution has to follow. Though systems can be absolutely ordered by their functional complexity, the resulting relation is not a linear order but a partial order: in general, it is not possible to determine which of two arbitrarily chosen systems is most functionally complex. For example, there is no absolute way in which one can decide whether a system that can survive situations A, B and C is more or less complex or fit than a system that can survive C, D and E. Yet, one can state that both systems are absolutely less fit than a system that can survive all A, B, C, D and E. Mathematically, such a partial order can be defined by the inclusion relation operating on the set of all sets of situations or perturbations that the system can survive. This also implies that there are many, mutually incomparable ways in which a system can increase its absolute fitness. For example, the first mentioned system might add either D or E to the set of situations it can cope with. The number of possibilities is infinite. This leaves evolution wholly unpredictable and open-ended.

For example, though humans are in all likeliness absolutely more functionally complex than snails or frogs, evolution might well have produced a species that is very different from humans, yet is similarly at a much higher functional complexity level compared to the other species. In perhaps slightly different circumstances, the Earth might have seen the emergence of a civilisation of intelligent dogs, dolphins or octopuses. It is likely that analogous evolutions are taking place or have taken place on other planets. Though humanity seems to have reached the highest level of functional complexity in the part of evolution that we know, more intelligent and complex species may well exist elsewhere in the universe, or may appear after us on Earth. The conclusion is that a preferred direction for evolution in the present, generalized sense does not in any way support the ideology of anthropocentrism.

References:

# Evolutionary Systems

Author: S.N. Salthe
Updated: Feb, l993
Filename: EVOLSYS.html

 Synopsys: Working Definition: Systems of whatever material base that undergo evolutionary processes.

In the important characterizing sources (Laszlo, l987; Csanyi, l989 see also Goonatilake, l99l) these have been restricted to living and linguistic systems (focusing on genetic information and its consequences) but the work of, e.g., Prigogine's school on dissipative structures, opens up the high probability that minimally some abiotic systems are also "evolutionary". Unless this is so the origin of life will remain an unapproachable puzzle.

## Characterization:

The field has its sources in general systems theory and, I believe, in structuralism. Both are concerned with structural commonalities (in both form and behavior) between systems of different material embodiment. Other fields currently feeding into the basic construction of evolutionary systems include non-equilibrium thermodynamics, information theory, cybernetics (of the "second" kind), hierarchy theory (to the extent that its contribution is distinct from that of general systems), dynamical systems, constructionist mathematics, category theory, semiotics, and philosophical analysis is crucially important at the present time.

Mainstream synthetic evolutionary theory from biology (neodarwinan population biology) has had little impact here , although it could be said that most workers involved accept natural selection as being involved in greater or lesser degree, at one or more scalar levels; the field, however, does not appear in most hands to be driven by this concept. This is especially so because most studies focus on the regularities of change (what darwinians might term ' evolutionary trends', which for them are extremely problematic and always require careful qualification so as to avoid "teleology").

Here we uncover an important difference between standard darwinian thought and that in evolutionary systems. The former is resolutely mechanistic, while most versions of the latter, more or less explicitly, tentatively at least, reject mechanistic materialism as their basic philosophy. On this score we find interest in ideas like self-organization, autopoiesis, autogenesis, autocatakinesis, autognosis, semiosis and other ideas linked to change being generated from within a changing system rather than from outside in newtonian/darwinian style, and to the necessity for bringing the subjective observer explicitly into representations.

## C) Current problems which would form the basis of discussions at a conference

(1) understanding the need for this field at the present time

(2) agreeing on working definitions of basic terms: e.g., evolution, self-organization, development, emergence

(3) comparisons of currently competing concepts - autopoiesis, autogenesis, autocatakinesis, etc.

(4) how to formalize non-mechanistic systems

(5) the role of semiosis in natural science

(6) seeking the appropriate role of natural selection in evolutionary systems

(7) The problems of self-referential systems

(8) the problem of subjectivity in science

(9) reviewing again the ever present confusions about thermodynamics, as well as its role in evolutionary systems

References:

Csanyi, V., l989. Evolutionary Systems and Society: a General Theory. Duke University Press.

Goonatilake, S., l99l. The Evolution of Information: Lineages in Gene, Culture and Artefact. Pinter Publishers.

Laszlo, E., l987. Evolution: the Grand Synthesis. Shambala.

# Cybernetics

Author: F. Heylighen,
Updated: Feb 17, 1997
Filename: CYBERN.html

 Synopsys: cybernetics studies organization, communication and control in complex systems by focusing on circular (feedback) mechanisms

Cybernetics, deriving from the Greek word for steersman (kybernetes), was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in society and in individual human beings). It grew out of Shannon's information theory, which was designed to optimize the transmission of information through communication channels, and the feedback concept used in engineering control systems. In its present incarnation of "second-order cybernetics", its emphasis is on how observers construct models of the systems with which they interact (see constructivism).

The main emphasis of cybernetics is on the circular mechanisms that allow complex systems to maintain, adapt, and self-organize. Such circularity or self-reference makes it possible to make precise, scientific models of purposeful activity, that is, behavior that is oriented towards a goal or preferred condition. In that sense, cybernetics proposes a revolution with respect to the linear, mechanistic models of traditional Newtonian science. In classical science, every process is determined solely by its cause, that is, a factor residing in the past. However, the behavior of living organisms is typically teleonomic, that is, oriented towards a future state, which does not exist as yet.

Cybernetics has discovered that teleonomy (or finality) and causality can be reconciled by using non-linear, circular mechanisms, where the cause equals the effect. The simplest example of such a circular mechanism is feedback. The simplest application of negative feedback for self-maintenance is homeostasis. The non-linear interaction between the homeostatic or goal-directed system and its environment results in a relation of control of the system over the perturbations coming from the environment.

# Homeostasis

Author: J. de Rosnay
Updated: Feb 17, 1997
Filename: HOMEOSTA.html

 Synopsys: Homeostasis: resistance to change

A person threatened by the environment (or informed of an approaching pleasure or danger) prepares for action. The body mobilizes reserves of energy and produces certain hormones such as adrenalin, which prepare it for conflict or flight. This mobilisation can be seen in familiar physiological reactions. In the presence of emotion, danger, or physical effort the heart beats faster and respiration quickens. The face turns red or pales and the body perspires. The individual may experience shortness of breath, cold sweats, shivering, trembling legs. These physiological manifestations reflect the efforts of the body to maintain its internal equilibrium. Action can be voluntary--to drink when one is thirsty, to eat when hungry, to put on clothing when cold, to open a window when one is too warm--or involuntary--shivering, sweating.

The internal equilibrium of the body, the ultimate gauge of its proper functioning, involves the maintenance of a constant rate of concentration in the blood of certain molecules and ions that are essential to life and the maintenance at specified levels of other physical parameters such as temperature. This is accomplished in spite of modifications of the environment.

This extraordinary property of the body has intrigued many physiologists. In 1865 Claude Bernard noticed, in his Introduction to Experimental Medicine. that the "constancy of the internal milieu was the essential condition to a free life." But it was necessary to find a concept that would make it possible to link together the mechanisms that effected the regulation of the body. The credit for this concept goes to the American physiologist Walter Cannon. In 1932, impressed by "the wisdom of the body" capable of guaranteeing with such efficiency the control of the physiological equilibrium, Cannon coined the word homeostasis from two Greek words meaning to remain the same. Since then the concept of homeostasy has had a central position in the field of cybernetics.

Homeostasis is one of the most remarkable and most typical properties of highly complex open systems. A homeostatic system (an industrial firm, a large organization, a cell) is an open system that maintains its structure and functions by means of a multiplicity of dynamic equilibriums rigorously controlled by interdependent regulation mechanisms. Such a system reacts to every change in the environment, or to every random disturbance, through a series of modifications of equal size and opposite direction to those that created the disturbance. The goal of these modifications is to maintain the internal balances.

Ecological, biological, and social systems are homeostatic. They oppose change with every means at their disposal. If the system does not succeed in reestablishing its equilibriums, it enters into another mode of behavior, one with constraints often more severe than the previous ones. This mode can lead to the destruction of the system if the disturbances persist.

Complex systems must have homeostasis to maintain stability and to survive. At the same time it bestows on the systems very special properties. Homeostatic systems are ultrastable; everything in their internal, structural, and functional organization contributes to the maintenance of the same organization. Their behavior is unpredictable; "counterintuitive" according to Jay Forrester, or contravariant: when one expects a determined reaction as the result of a precise action, a completely unexpected and often contrary action occurs instead. These are the gambles of interdependence and homeostasis; statesmen, business leaders, and sociologists know the effects only too well.

For a complex system, to endure is not enough; it must adapt itself to modifications of the environment and it must evolve. Otherwise outside forces will soon disorganize and destroy it. The paradoxical situation that confronts all those responsible for the maintenance and evolution of a complex system, whether the system be a state, a large organization, or an industry, can be expressed in the simple question, How can a stable organization whose goal is to maintain itself and endure be able to change and evolve?

# Principles of Systems and Cybernetics

Author: F. Heylighen, C. Joslyn,
Updated: Mar 22, 1994
Filename: CYBSPRIN.html

Principles or laws play the role of expressing the most basic ideas in a science, establishing a framework or methodology for problem-solving. The domain of General Systems and Cybernetics is in particular need of such principles, since it purports to guide thought in general, not just in a specific discipline. Unfortunately, the few generally used principles of the domain, such as the law of requisite variety, or the principle that the whole is more than the sum of its parts, are typically ambiguous or controversial, and lack coherence with each other.

The heart of the Principia Cybernetica Project lies therefore in the establishment of a set of clear and coherent Principles of Cybernetics. As Principles, they should have the following properties:

Primitive:
The word "principle" is derived from the Latin principes (he who goes first) or primo (first). Principles are primary or primitive in that something can follow from them. They are the beginning to a system of thought, axiomatic (although we want to avoid the formal sense of that term).
Simplicity:
Principles should be simple almost to the point of being self-evident or tautological.
Universality:
Principles should have virtually universal applicability within the domain of Cybernetics and Systems Science.

The preliminary set of principles we propose are based on and expressed in terms of a set of primitive concepts of systems and cybernetics. Some of the principles have a long and venerable tradition in Cybernetics and Systems Science, while others are novel to Metasystem Transition Theory. Our analysis will on the one hand critically assess existing principles, clarifying their meaning, on the other hand try to formulate new principles which may generalize or interconnect known laws.

The ultimate goal is to arrive at a network of concepts and principles similar to a formal system, with "axioms" implicitly defining primitive concepts, definitions of higher order concepts, and "theorems", derived from the more primitive axioms and definitions. The fundamental principles, like all good axioms, are supposed to be self-evident. Their implications, like most theorems, on the other hand, may be far from trivial, and sometimes even counter-intuitive.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# Occam's Razor

Author: F. Heylighen,
Updated: Jul 7, 1997
Filename: OCCAMRAZ.html

 Synopsys: one should not increase, beyond what is necessary, the number of entities required to explain anything

Occam's razor is a logical principle attributed to the mediaeval philosopher (William of Occam (or Ockham). The principle states that one should not make more assumptions than the minimum needed. This principle is often called the principle of parsimony. It underlies all scientific modelling and theory building. It admonishes us to choose from a set of otherwise equivalent models of a given phenomenon the simplest one. In any given model, Occam's razor helps us to "cut away" those concepts, variables or constructs that are not really needed to explain the phenomenon. By doing that, developing the model will become much easier, and there is less chance of introducing inconsistencies, ambiguities and redundancies.

Though the principle may seem rather trivial, it is essential for model building because of what is known as the "underdetermination of theories by data". For a given set of observations or data, there is always an infinite number of possible models explaining those same data. This is because a model normally represents an infinite number of possible cases, of which the observed cases are only a finite subset. The non-observed cases are inferred by postulating general rules covering both actual and potential observations.

For example, through two data points in a diagram you can always draw a straight line, and induce that all further observations will lie on that line. However, you could also draw an infinite variety of the most complicated curves passing through those same two points, and these curves would fit the empirical data just as well. Only Occam's razor would in this case guide you in choosing the "straight" (i.e. linear) relation as best candidate model. A similar reasoning can be made for n data points lying in any kind of distribution.

Occam's razor is especially important for universal models such as the ones developed in General Systems Theory, mathematics or philosophy, because there the subject domain is of an unlimited complexity. If one starts with too complicated foundations for a theory that potentially encompasses the universe, the chances of getting any manageable model are very slim indeed. Moreover, the principle is sometimes the only remaining guideline when entering domains of such a high level of abstraction that no concrete tests or observations can decide between rival models. In mathematical modelling of systems, the principle can be made more concrete in the form of the principle of uncertainty maximization: from your data, induce that model which minimizes the number of additional assumptions.

This principle is part of epistemology, and can be motivated by the requirement of maximal simplicity of cognitive models. However, its significance might be extended to metaphysics if it is interpreted as saying that simpler models are more likely to be correct than complex ones, in other words, that "nature" prefers simplicity.

# The Identity of the Indistinguishables

Author: F Heylighen,
Updated: Sep 19, 1995
Filename: IDENINDI.html

 Synopsys: Two entities that do not have any properties allowing to distinguish them should be seen as a single entity

This principle was probably first formulated by the philosopher and mathematician Gottfried Wilhelm Leibniz (a precursor of cybernetics and artificial intelligence, with his "calculus of thought") in the Monadology. It can be derived from the principle of Occam's razor, which implies that if there is no reason why two things should be distinguished then it is better to just identify them, so that one entity is left rather than two.

A similar idea underlies the Pauli Exclusion Principle in quantum physics, which states that no two particles ("fermions") can be in the same state at the same moment (otherwise, they would be indistinguishable and therefore there would be only a single particle). A more modern, cybernetic formulation, used by Gregory Bateson, is that of "a difference that makes a difference": distinctions are only useful insofar that they lead to further distinctions. If the initial distinction does not allow you to make any further distinctions, you should better drop it. This "relational" interpretation where the value of a distinctions depends on the further distinctions it is connected with can be formalized as the "bootstrapping axiom". It also underlies the microscopic interpretation of the principle of causality (identic causes have identic results), and a generalized version of empiricism, which says that postulated entities in a theory should (directly or indirectly) lead to observable differences.

# Downward Causation

Author: F. Heylighen,
Updated: Sep 15, 1995
Filename: DOWNCAUS.html

 Synopsys: all processes at the lower level of a hierarchy are restrained by and act in conformity to the laws of the higher level

This is how Donald T. Campbell (1974) originally formulated the principle of downward causation. Let us try to clarify what this means. Reductionism can be defined as the belief that the behavior of a whole or system is completely determined by the behavior of the parts, elements or subsystems. In other words, if you know the laws governing the behavior of the parts, you should be able to deduce the laws governing the behavior of the whole.

Systems theory has always taken an anti-reductionist stance, noting that the whole is more than the sum of the parts. In other words, the whole has "emergent properties" which cannot be reduced to properties of the parts. Since emergence is a rather slippery concept, which has been defined in many different ways, most of which are highly ambiguous or fuzzy, I prefer to express this idea with the more precise concept of downward causation.

Downward causation can be defined as a converse of the reductionist principle above: the behavior of the parts (down) is determined by the behavior of the whole (up), so determination moves downward instead of upward. The difference is that determination is not complete. This makes it possible to formulate a clear systemic stance, without lapsing into either the extremes of reductionism or of holism:

 the whole is to some degree constrained by the parts (upward causation), but at the same time the parts are to some degree constrained by the whole (downward causation).

Let me illustrate this with an example. It is well-known that snow crystals have a strict 6-fold symmetry, but at the same time that each crystal has a unique symmetric shape. The symmetry of the crystal (whole) is clearly determined by the physico-chemical properties of the water molecules which constitute it. But on the other hand, the shape of the complete crystal is not determined by the molecules. Once a shape has been formed, though, the molecules in the crystal are constrained: they can only be present at particular places allowed in the symmetric crystalline shape. The whole (crystal) constrains or "causes" the positions of the parts (molecules).

The appearance of this "two way causation" can be explained in the following way. Imagine a complex dynamic system. The trajectories of the system through its state space are constrained by the "laws" of the dynamics. These dynamics in general determine a set of "attractors": regions in the state space the system can enter but not leave. However, the initial state of the system, and thus the attractor the system will eventually reach is not determined. The smallest fluctuations can push the system either in the one attractor regime or the other. However, once an attractor is reached, the system loses its freedom to go outside the attractor, and its state is strongly constrained.

Now equate the dynamics with the rules governing the molecules, and the attractor with the eventual crystal shape. The dynamics to some degree determines the possible attractors (e.g. you cannot have a crystal with a 7-fold symmetry), but which attractor will be eventually reached is totally unpredictable from the point of view of the molecules. It rather depends on uncontrollable outside influences. But once the attractor is reached, it strictly governs the further movement of the molecules.

The same principle applies to less rigid, mechanistic systems such as living organisms. You cannot have organisms whose internal functioning flouts the rules of physics and chemistry. However, the laws of physics are completely insufficient to determine which shapes or organizations will evolve in the living world. Once a particular biological organization has emerged, it will strongly constrain the behavior of its components.

For example, the coding of amino acids by specific triplets of bases in the DNA is not determined by any physical law. A given triplet might as well be translated into a multitude of other amino acids than the one chosen in the organisms we know. But evolution happens to have selected one specific "attractor" regime where the coding relation is unambiguously fixed, and transgressions of that coding will be treated as translation errors and therefore eliminated by the cell's repair mechanisms.

A final example from the cultural sphere. Although our basic measuring units (e.g. second or meter) are defined by physical means (e.g. through the invariant length of a particular wave lenght of a particular type of electromagnetic radiation), the specific choice of unit is wholly arbitrary. The laws of physics impose the constraint that the wave lenght of light emitted by a particular quantum transition as measured in the units we choose must always be the same. However, the choice of a particular unit is not determined by those laws of physics. It is the result of a complex socio-cultural evolution in which different units are proposed for the most diverse reasons, after which one unit is eventually selected, perhaps because it has been used a little bit more frequently by slightly more authoritative sources than the other ones. Once the standard gets established, it becomes a constraint which everybody is supposed to follow. The whole (the socio-cultural system with its standards) determines the behavior of the parts (the measurements made by individuals).

References:

• Campbell D.T. (1990): "Levels of Organization, Downward Causation, and the Selection-Theory Approach to Evolutionary Epistemology", in: Scientific Methodology in the Study of Mind: evolutionary epistemology, E. Tobach and G. Greenberg (ed.), (Erlbaum, Hillsdale, NJ), p. 1-17.
• Campbell D.T. (1974): "'Downward causation' in Hierarchically Organized Biological Systems", in: Studies in the Philosophy of Biology, F.J. Ayala & T. Dobzhansky (ed.), (Macmillan Press), p. 179-186

# The Principle of Causality

Author: F. Heylighen,
Updated: Sep 19, 1995
Filename: PRINCAUS.html

 Synopsys: equal causes have equal effects (1)

This proposition is to be understood in both directions: "equal effects have (are the results of) equal causes" applies as well.

The former expression denotes predictability: if we know the effect e1 of c1, and we know that another cause c2 is equal to c1, then we can predict that c2 will have an effect e2 equal to e1. The latter (converse) expression denotes reversibility: if we know two equal effects e2 and e1, and we know that e1 was caused by c1, then we can retrodict the cause c2 equal to c1 of e2, that is to say we can reverse the process (at least informationally). The whole expression can also be reformulated as :

 distinct causes have distinct effects (and vice-versa). (2)

This expression can be summarized as distinction conservation: if two situations were initially distinct they will remain distinct in all further evolutions, and have been distinct during all previous evolutions.

An equivalent principle defining causality is that of covariation:

 when a cause is varied (replaced by a cause different in some respect), its associated effect will vary (3)

The problem with these different definitions is that if they are considered at the most fundamental, microscopic level, they become trivial, unable to produce any practical predictions. In (1), if "equal" is taken to mean "identic" we get the tautologous result that if a cause is identic to itself (which it always is), then its effect must be identic to itself (which can be seen as a paraphrase of the principle of the identity of the indistinguishables). In (2) or (3) we get that if an initial state is replaced by a state that is not identically the same, then the resulting state will not be identically the same. If the state is a "state of the world", encompassing all that exists, then it is obvious that after a change of that state the resulting state will not be identically the same as the one resulting before the change. (Otherwise we would not be able to retrodictively distinguish the two initial states, since no difference whatsoever would be left).

We may call such a radical interpretation of (1) and (2) the "microscopic causality" principle. This principle does not allow us to make any predictions. In practice, "equal" is therefore interpreted as "similar": two causes or events may be different in some way, yet behave the same as far as the things we are interested in are concerned.

However, recent advances in non-linear dynamics have shown that in general dynamic systems are chaotic, in the sense that small differences in initial conditions can lead to enormous differences in later conditions, so that any "similarity" between two initial states is lost. In such cases the causality principle becomes practically meaningless.

For example, consider a coin vertically resting on its side in an unstable equilibrium. When it falls, we will not look for the cause of it falling down this side rather than that side, but rather consider it a chance event. The reason is that the coin in an unstable equilibrium is so sensitive that it will covary with the tiniest variation in its neighbourhood: a little bit of wind, a vibration of the table, an irregularity in the surface, or even a random distribution of air molecules so that a little bit more molecules bump against one side rather than against the other. The coin follows the microscopic causality principle: the slightest difference in initial conditions makes a difference in results.

In practice, the causality principles mentioned above only make sense when equality or distinction can be interpreted macroscopically, by means of boundaries between equivalence classes of causes and effects, so that microscopic differences between causes within one equivalence class can be ignored and only the macroscopic differences between the classes need to be taken into account. This is "macroscopic causality". However, macroscopic causality will only work in certain cases (e.g. when the dynamics is non-chaotic), and therefore cannot be seen as a universal principle.

The classical world view can be characterized by the assumption that macroscopic causality, or more generally distinction conservation, is always valid. The non-classical view, which appears in quantum mechanics and far-from-equilibrium thermodynamics, is then characterized by distinction non-conservation. That means that initially distinct states may lead to the same result (distinction destruction), or that the same state might lead to distinct outcomes (distinction creation). This requires more general principles of evolution and self-organization, based on variation and selection. The fact that macroscopic causality itself works in certain cases may be explained by these principles.

Reference: Heylighen F. (1989): "Causality as Distinction Conservation: a theory of predictability, reversibility and time order", Cybernetics and Systems 20, p. 361-384.

# Control

Author: V. Turchin, F. Heylighen, C. Joslyn, & J. Bollen,
Updated: Oct 21, 1996
Filename: CONTROL.html

Control is the operation mode of a control system which includes two subsystems: controlling (a controller) C, and controlled, S. They interact, but there is a difference between the action of C on S, and the action of S on C. The controller C may change the state of the controlled system S in any way, including the destruction of S. The action of S on C is formation of a perception of system S in the controller C. This understanding of control is presented in Fig.1.

In Fig.2 we define the concept of perception. We see in the controller an agent which is responsible for its actions, and a representation of the controlled system, which is an object whose states we identify with perceptions. The relation between representation and agent is described as a flow of information: the actions of the agent depend on this flow. Thus the action of S on C is limited, in its effect, by changing only S's representation in C, not the rest of the system. Thus the asymmetry of the control relation: C controls S, but S does not control C. The action of S on C is "filtered" through the representation: its effect on C cannot be greater than allowed by the changing state of the representation.

Of course, two systems can be in a state of mutual control, but this will be a different, more complex, relation, which we will still describe as a combination of two asymmetric control relations.

In many cases the controlled system can be also seen in greater detail, which is done in Fig.3. We describe the controlled system using some variables and distinguish between the variables directly affected by the controller, from the variables which are observed by the controller in perception. The causal dependence of the observed variables on the affected variables is determined by the intrinsic dynamics of the system. We also must not forget about the effect of uncontrollable disturbances on the observed variables.

In Fig.3 we also made an addition to the controller: it now includes one more object which influences the agent: goal. The agent compares the current representation with the goal and takes actions which tend to minimize the difference between them. This is known as purposeful behavior. It does not necessarily result from the existence of an objectified goal; the goal may be built into the system -- dissolved in it, so to say. But a typical control system would include a goal as a an identifiable subsystem.

Even though the relation of control is asymmetric, it includes a closed loop. Looked from the controller, the loop starts with its action and is followed by a perception, which is an action in the opposite direction: from the controlled to the controller. This aspect of control relation is known as feedback.

The concept of control is the cornerstone of cybernetics. The basic control scheme which we have defined in this node is the unit from which complicated cybernetic systems are created by nature and man. For this reason, our definition is pretty wide: we want our building unit to be as universal as possible. In particular, we see as special cases of control some systems which most present authors would, probably, not call control.

The different components (e.g. perception, action, ...) of the control loop we have enumerated can in the limit be absent. This leads to different "degenerate" or "limit" cases, which we would not usually see as control systems, but which still share many properties with the more elaborate control scheme. Specific instances of this control scheme can further differ in the presence or absence of different attributes or properties characterizing control systems: separability, contingency, evolvability, asymmetry, ... This leads to a very broad view of control in which many very important types of system can be classified, as shown by many examples of control systems. The abstract scheme can also be mapped on different other schemes for control by authors such as Ashby, Powers and Meystel, and on an older definition in terms of statements and commands.

# Special Cases of Control

Author: V. Turchin, & F. Heylighen,
Updated: Oct 21, 1996
Filename: SPECCTRL.html

Our scheme defining the phenomenon of control contains a number of distinct components and properties. These components can in the limit be absent (have the value zero). The resulting schemes can be seen as special cases ("limit" or "degenerate" cases) of the more general scheme. The presence of the other components implies that they will stil inherit most of the properties of control from the more general scheme:

• the action part may be absent or ignored, so that the control relation is reduced to the formation in the controller of a perception of the controlled system. An example of such a relation can be seen in metalanguage and metatheory.
• If the perception part is absent or grossly inadequate, we face a situation of blind control.
• In a primitive control system there may be no representation of the controlled system S as distinct from S itself. Information flows directly from S to the controller C. The classical harmonic oscillator is an example of such a control system.
• the latter situation is a specific case of the more general phenomenon where the asymmetry between controller and controlled is absent, that is to say where the action of controller C on controlled S is not fundamentally different from the action of S on C. Other examples of such symmetric interaction include: communication between two control systems (interaction without fixed goals), conflict (interaction between two control systems with distinct goals), and cooperation or bonds (interaction between two control systems with the same goals).

# Metalanguages and Metarepresentations

Author: V. Turchin,
Updated: Oct 21, 1996
Filename: METALARE.html

We create a metalanguage and a metatheory in order to describe and examine some class of languages and theories. A metatheory M includes a representation of the examined theory T, and it can be seen as the perception in a control system where T is controlled by M. The result of the examination of the theories like T would, typically, be classification, modification and creation anew of the theories from the class to which T belongs. This is the action part of the control relation between T and M. This part, though, does not enter the meaning of the terms metalanguage' and metatheory': it may be absent. Hence we deal here with the case of a control system devoid of action.

# Blind control

Author: V. Turchin,
Updated: Oct 21, 1996
Filename: BLINCTRL.html

We speak of blind control when the perception part of the control scheme is absent or grossly inadequate. No feed-back. Examples are numerous. The driver of a car gets asleep. The engine works on and drives the car ... into the ditch.

A less tragic example is a programmable electric oven which executes its program without any feed-back from the cooked food.

# The Harmonic Oscillator as a Control System

Author: V. Turchin,
Updated: Oct 21, 1996
Filename: HARMOSCL.html

The classical harmonic oscillator is an example of a primitive control system where there is no representation of the controlled system S as distinct from S itself. Information flows directly from S to the controller C.

A hydrogen molecule can be treated as a harmonic oscillator. Here the controlled system S consists of two protons at a distance x from each other. The agent which is embedded in S is the relative momentum of the protons in the center-of-mass coordinate system; it causes protons to move with respect to each other.

The controller C is the electron shell which holds the protons together at a certain equilibrium distance x0 by exerting on them a force which acts against the coulomb repulsion. (This force is quantum-mechanical in its origins, but this does not prevent us from considering oscillations classically). The distance x is a representation of the two-proton controlled system for the controller, the shell, but there is no part of the controller that would keep x: it is a feature of the proton pair, i.e. the controlled system. Also, there is no separate subsystem to keep the goal of the controller, but it functions as if the goal was to keep the distance x close to x0.

We shall now see that the control processes in a classical oscillator result from the interplay of the three fundamental factors in classical mechanics: coordinate, momentum, and force. The three arrows in our control scheme: action, perception and information will be described by the three familiar equations of the classical oscillator.

Nothing happens as long as the protons are at the equilibrium distance x0 and at rest with respect to each other. Suppose some uncontrollable disturbance pushes one of the protons passing to the controlled system some momentum p. The coordinate x starts changing according to the equation:

dx/dt = p/m (perception)

In terms of the control scheme this is perception, because the agent p of the controlled system determines the representation.

The change in the representation informs the agent F of the controller, the shell, which is assumed to be proportional to the change of x and opposite in direction:

F = -k(x-x0) (information)

This force acts on the proton pair, causing the change of the momentum according to the equation:

dp/dt = F (action)

This will start stopping the movement of the protons and finally will reverse it. In this manner, unstopping oscillations will follow, which will keep the distance x close to x0 (assuming that the initial push was in certain limits).

# Examples and Counterexamples of Control Systems (empty)

Author: F. Heylighen,
Updated: Oct 21, 1996
Filename: EXAMCTRL.html

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# Properties of a Control System

Filename: PROPCTRL.html

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Properties of a Control System, Spatial separability of controller from controlled, Temporal separability of controller from controlled, Contingency of perception-action relation, Evolvability of the Controller, Filtering of the Controlled, Amplification of the Control Signal,

# Other Definitions of Control

Author: F. Heylighen, & Joslyn,
Updated: Oct 21, 1996
Filename: ISOMCTRL.html

There are different alternative definitions of control to the one we are proposing in the Principia Cybernetica Web, including a definition in terms of a feedback with two inputs and amplification and a definition in terms of statements and commands. These definitions are either equivalent to our definition or can be seen as special cases of it.

# Powers' Definition of Control

Author: F. Heylighen,
Updated: Oct 21, 1996
Filename: POWRCTRL.html

William T. Powers, the founder of Control Theory, has proposed the following general scheme to represent negative feedback control mechanisms. This scheme is very similar to our scheme for control except that it tries to be quantitative, whereas ours is qualitative. By the fact that it is quantitative, there are no sharp distinctions between different cases of control, but only larger or smaller values of the relevant variables. This is in particular interesting for the asymmetry of control, which is automatically assumed in our scheme, while in Powers' scheme it derives from the fact that one variable is much larger than another one.

The scheme consist of a feedback loop with two inputs, the reference signal r (equivalent to our "goal") and the disturbance d. The arm of the loop going from r to d is called the action, a. The one going from d to r is called the perception, p. The relation between these elements should be such that the perception is brought as closely as possible to the reference, by the action compensating for the effect of the disturbance. In the simplest case, each component can be represented as a one dimensional variable, and the relation between them by two linear expressions:

 a = K (r - p) (1) p = E (d - a) (2)

The action is a function of the difference between the reference level and the perceived level. The larger the deviation between perception and goal ("error"), the larger the correcting action needed. Similarly, the perceived state is a function of the difference between the disturbance and the compensating action. K and E are two constants expressing characteristics of the control system. To better understand their meaning, the system of equations can be solved for a and p. This produces the following expressions:

 a = KE(r/E - d)/(KE + 1) (3) p = KE(r - d/K)/(KE - 1) (4)

Until now, the whole scheme is symmetric with respect to permutations of (r, a) with (d, p). In Fig 1, we can turn the loop upside down, and then the reference becomes the disturbance and the action becomes the perception. However, this does not accord with our intuitive understanding of control as an asymmetric relation, in which the controller (r and a) controls the perturbations (d and p), but not the other way around. The asymmetry can be introduced by assuming that K is very large, and much larger than E. In that case, through equation (4), p becomes almost equal to r:

K >> E => p ~= r

This means that control is achieved: the deviation between goal and perception is practically reduced to zero. Whatever the size of the disturbance d, the control system manages to keep the state very close to its preferred state r. K expresses the factor of "amplification": the smallest deviation (r - p) is immediately sensed and compensated by a large action a = K (r - p). Although the action is large relative to the deviation, as expressed by eq. (3), it does not need to be large in absolute terms. The reason is that because of the negative feedback relation the deviation is immediately compensated before it can grow larger. Thus, really large actions will never be needed, since deviations always remain small. The amplification factor measures both the "power" of the control system, in the sense of the amount of energy it has available to take action, and its "sensitivity", in the sense of the precision with which is responds to the tiniest deviations. Since the scheme is symmetric in E and K, we can make a similar interpretation of the implications of a large factor E. This would mean that the perception is very sensitive to disturbances, in other words that the tiniest perturbation would make the system deviate from the reference level. This describes a situation with poor or no control. In conclusion, the larger K with respect to E, the less the perception will deviate from the goal, and the better the control.

# Control in Terms of Statements and Commands

Author: V. Turchin,
Updated: Sep 1991
Filename: CONTROSC.html

We know that there are two types of messages: descriptive, or statements, and imperative, or commands. The combination of the two creates a relation which is known as control. A system C controls system S if C receives from S statements and sends to S commands. Actually, this is a special case which is predominant in well-organized cybernetic systems. In the general case, the controlling system C also may directly alter the states of the controlled system S. The difference between direct intervention and sending a command is relative, though. It depends on the definition of the systems involved.

Example: you drive a car. Here C is you, and S is your environment: the car, the road, etc. You receive visual and audio messages from the environment and vary the position of the car's wheels to keep it on the road. This is a direct change of the state of the controlled system. But you can consider your steering wheel as an information channel, through which you send commands to the wheels proper.

Another example: remote control of a missile. Here you receive information from the missile's sensors and send commands to it. But if you do not wish, for some reason (although this is not really reasonable) to separate the information channel from the missile and consider them together as the controlled system S, then you exercize a direct intervention.

The relation between a language and metalanguage, or theory and metatheory, is also a control relation. Here S is the system that uses the language, and C is the system that uses metalanguage. We create metalanguage and metatheory in order to examine the works of the language and the theory. The flow of descriptive information is from the language S to the metalanguage C. The result of our examination of the language and theory is classification and construction of more sentences of the language and more theories - this is alteration of the state of the language S.

# Feedback

Author: J. de Rosnay
Updated: Jan 6, 1997
Filename: FEEDBACK.html

In a system where a transformation occurs, there are inputs and outputs. The inputs are the result of the environment's influence on the system, and the outputs are the influence of the system on the environment. Input and output are separated by a duration of time, as in before and after, or past and present.

In every feedback loop, as the name suggests, information about the result of a transformation or an action is sent back to the input of the system in the form of input data. If these new data facilitate and accelerate the transformation in the same direction as the preceding results, they are positive feedback - their effects are cumulative. If the new data produce a result in the opposite direction to previous results, they are negative feedback - their effects stabilize the system. In the first case there is exponential growth or decline; in the second there is maintenance of the equilibrium.

Positive feedback leads to divergent behavior: indefinite expansion or explosion (a running away toward infinity) or total blocking of activities (a running away toward zero). Each plus involves another plus; there is a snowball effect. The examples are numerous: chain reaction, population explosion, industrial expansion, capital invested at compound interest, inflation, proliferation of cancer cells. However, when minus leads to another minus, events come to a standstill. Typical examples are bankruptcy and economic depression.

In either case a positive feedback loop left to itself can lead only to the destruction of the system, through explosion or through the blocking of all its functions. The wild behavior of positive loops - a veritable death wish - must be controlled by negative loops. This control is essential for a system to maintain itself in the course of time.

Negative feedback leads to adaptive, or goal-seeking behavior: sustaining the same level, temperature, concentration, speed, direction. In some cases the goal is self-determined and is preserved in the face of evolution: the system has produced its own purpose (to maintain, for example, the composition of the air or the oceans in the ecosystem or the concentration of glucose in the blood). In other cases man has determined the goals of the machines (automats and servomechanisms). In a negative loop every variation toward a plus triggers a correction toward the minus, and vice versa. There is tight control; the system oscillates around an ideal equilibrium that it never attains. A thermostat or a water tank equipped with a float are simple examples of regulation by negative feedback.

# Semantic Control

Author: C. Joslyn, F. Heylighen,
Updated: Aug 1993
Filename: SEMCONT.html

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(see the paper "Semantic Control Systems").

# Semiotic Terms

Author: C. Joslyn,
Updated: May 8, 1998
Filename: SEMIOTER.html

4. The following are mostly from within the theory of semantics and control as developed by Joslyn (see the paper "Semantic Control Systems").
5. They have been supplemented from the Glossary of Semiotics, by Vincent Colapietro [ CoV93].
6. The general categories considered are:

Signs and sign-functions
Signifiers and tokens
Signifieds and objects
Degrees of motivation and arbitrariness
Proper signs
Symbols and codes
Laws and semantic closures

Sign :
A deterministic, functional regularity or stability in a system, also sometimes called a sign-function. Something, the signifier, stands for something else, the signified, in virtue of the sign-function. May be either lawful, proper, or symbolic depending on the presence or absence of motivation. This is, of course, a very general definition, but it is in the tradition of both semiotics and general systems theory to think very generally.
Contains: signifier, signified.
Cases: lawful, proper, symbolic.
Synonym: sign function.
Sign Function :
Synonym: sign.

Signifier :
That part of a sign which stands for the signified, for example a word or a DNA codon.
Synonym: token, sign vehicle.
Part-of: sign.
Token :
The physical entity or marker which manifests the signifer by standing for the signified.
Synonym: signifier, sign vehicle.
Sign Vehicle :
Synonym: token, signifier.

Signified :
That part of a sign which is stood for by the signifier. Sometimes thought of as the meaning of the signifier.
Synonym: object, referent, interpretant.
Part-of: sign.
Object :
Synonym: signified, referent, interpretant.
Referent :
Synonym: signified, object, interpretant.

Motivation :
The presence of some degree of necessity between the signified and siginifier of a sign. Makes the sign proper, and complete motivation makes the sign lawful. For example, a painting may resemble its subject, making it a proper sign.
Antonym: arbitrariness.
Arbitrariness :
The absence of any degree of necessity between the signified and siginifier of a sign. Makes the sign symbolic. For example, in English we say "bachelor" to refer to an unmarried man, but since we might just as well say "foobar", therefore "bachelor" is a symbol.
Antonym: motivation.

Proper Sign :
A sign which has an intermediate degree of motivation. For example, a photograph is a proper sign.
isa: sign.
Cases: icon, index.
Icon :
A proper sign where the motivation is due to some kind of physical resemblance or similarity between the signified and siginifier. For example, a map is an icon of its territory.
isa: proper sign.
Index :
A proper sign where the motivation is due to some kind of physical connection or causal relation between the signified and siginifier. For example, smoke is an index of fire.
isa: proper sign.

Symbol :
For CS Peirce, a sign where the sign function is a conventional rule or coding. The operation of a symbol is dependent on a process of interpretation.
isa: sign.
Rule :
A functional regularity or stability which is conventional, and thus necessary within the system which manifests it, but within a wider universe it is contingent, or arbitrary. For example, if we wish to refer to an unmarried man in English, then we must say "bachelor", even though "bachelor" is a symbol.
Synonym: code, semantic relation.
Antonym: law.
Semantic Relation :
Synonym: code, rule.
Code :
The establishment of a conventional rule-following relation in a symbol, represented as a deterministic, functional relation between two sets of entities.
Synonym: semantic relation, rule.
Interpret :
To take something for something else in virtue of a coding.
Interpreter :
That entity, typically a human subject, which interprets the sign vehicle of a symbol.
Interpretant :
For Peirce, that which followed semantically from the process of interpretation.
Synonym: signified, object, referent.

Law :
A regularity or stability which is necessary for all systems, and thus immutable as a fact of nature. The necessity of the relation is called the sign's motivation.
Antonym: rule.
Semantic Closure :
Propounded by Pattee [ PaH82], the property of real semiotic systems like organisms, wherein the interpreter is itself a referent of the semantic relation.

# The Law of Requisite Variety

Author: F. Heylighen, C. Joslyn,
Updated: Aug 1993
Filename: REQVAR.html

Joslyn:

Perhaps the most famous (and some would say the only successful) principle of cybernetics recognized by the whole Cybernetics and Systems Science community is Ashby's Law of Requisite Variety (LRV) \cite{ASR56}. The Law has many forms, but it is very simple and common sensical: a model system or controller can only model or control something to the extent that it has sufficient internal variety to represent it. For example, in order to make a choice between two alternatives, the controller must be able to represent at least two possibilities, and thus one distinction. From an alternative perspective, the quantity of variety that the model system or controller possesses provides an upper bound for the quantity of variety that can be controlled or modeled.

Requisite Variety has had a number of uses over the years \cite{DEJ87,POB76}, and there are a number of alternative formulations. As mentioned in section variety, variety can be quantified according to different distributions, for example probabilistic entropies and possibilistic nonspecificities. Under a stochastic formulation, there is a particularly interesting isomorphism between the LRV, the 2nd Law of Thermodynamics \cite{ZWM78c,STS87}, and Shannon's 10th Theorem \cite{SHCWEW64}.

Heylighen:

The larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate.

This is another application of the principle of selective variety, formulated above. However, a stronger form of Ashby's Law (1958), "the variety in the control system must be equal to or larger than the variety of the perturbations in order to maintain stability", does not hold in general. Indeed the underlying "only variety can destroy variety" assumption is in contradiction with the principle of asymmetric transitions which implies that spontaneous decrease of variety is possible. For example, the bacterium described above disposes of a minimal variety of only two actions: increase or decrease the rate of random movements. Yet, it is capable to cope with a quite complex environment, with many different types of perturbations (Powers, 1989). Its blind "transitions" are normally sufficient to find a favourable ("stable") situation, thus escaping all dangers.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# Law of Requisite Constraint

Author: C. Joslyn,
Updated: Jan 1992
Filename: REQCONS.html

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As is well known \cite{CORASR70}, in order for there to be a proper coordination of actions to perception the system must be able to select the correct choice. The ability of the system to avoid incorrect or unviable choices is a constraint on the behavior of the control system. If no such constraint existed, the system would have to try out actions blindly, and the larger the variety of perturbations, the smaller the probability that those actions would turn out to be adequate.

Thus all viable modeling and aniticipatory control requires an intermediate quantity of variety: enough to satisfy the LRV that it is possible to represent all necessary control systems; but not too much to violate the Law of Requisite Constraint and leave the system with insufficient "knowledge" about its environment.

This intermediary balance between freedom and constraint in viable systems has long been noted in information theory \cite{ZWM84a}. This can be measured in the intermediate entropy values that can be measured in symbol systems such as linguistic texts and chromosomes.

# The Law of Requisite Knowledge

Author: F. Heylighen,
Updated: Aug 1993
Filename: REQKNOW.html

 Synopsys: In order to adequately compensate perturbations, a control system must "know" which action to select from the variety of available actions

This principle reminds us that a variety of actions is not sufficient for effective control, the system must be able to (vicariously) select an appropriate one. Without knowledge, the system would have to try out an action blindly, and the larger the variety of perturbations, the smaller the probability that this action would turn out to be adequate. Notice the tension between this law and the law of selective variety: the more variety, the more difficult the selection to be made, and the more complex the requisite knowledge.

"Knowing" signifies that the internal (vicarious) selector must be a model or representation of the external, potentially selecting perturbations. Ideally, to every class of perturbations there corresponds a class of adequate counteractions. This correspondence might be represented as a homomorphism from the set of perturbations to the set of (equivalence classes of) compensations. However, this does not imply that knowledge would consist of a homomorphic image of the objects in the environment. Only the (perturbing) processes of the environment need to be represented, not its static structure.

An equivalent principle was formulated by Conant and Ashby (1970) as "Every good regulator of a system must be a model of that system". Therefore the present principle can also be called the law of regulatory models.

Reference: Heylighen F. (1992): "Principles of Systems and Cybernetics: an evolutionary perspective", in: Cybernetics and Systems '92, R. Trappl (ed.), (World Science, Singapore), p. 3-10.

# Law of Requisite Hierarchy

Author: C. Joslyn,
Updated: Sep 1, 1995
Filename: REQHIER.html

 Synopsys: The weaker the average regulatory ability and the larger the average uncertainty of available regulators, the more requisite hierarchy is needed in the organization of regulation and control for the same result of regulation

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The statement of the Law of Requisite Hierarchy by Arvid Aulin, is followed by:

". . . . [In other words], the lack of regulatory ability can be compensated to a certain extent by greater hierarchy in organization."
(Cybernetic Laws of Social Progress, p. 115)

# Closures

Updated: Aug 1993
Filename: CLOSURE.html

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Eigenvalues

Fixed points

Invariant relations

Symmetries

# The Metasystem Transition

Author: V. Turchin, C. Joslyn,
Updated: Aug 1993
Filename: MST.html

Consider a system S of any kind. Suppose that there is a way to make some number of copies from it, possibly with variations. Suppose that these systems are united into a new system S' which has the systems of the S type as its subsystems, and includes also an additional mechanism which controls the behavior and production of the S-subsystems. Then we call S' a metasystem with respect to S, and the creation of S' a metasystem transition. As a result of consecutive metasystem transitions a multilevel structure of control arises, which allows complicated forms of behavior.

In Turchin's book "The Phenomenon of Science" (Columbia University Press, 1977) it is shown that the major steps in evolution, both biological, and cultural, are nothing else but metasystem transitions of a large scale. The concept of metasystem transition allows us to introduce a kind of objective quantitative measure of evolution and distinguish between evolution in the positive direction, progress, and what we consider an evolution in the negative direction, regress (cf. the direction of evolution). In the present node we outline the main ideas of this book.

When we speak of cybernetic systems, we can describe them either in terms of their structures, or phenomenologically, in terms of their functioning. We cannot claim at the present time that we know the structure of the human brain well enough to explain thinking as the functioning of that structure. However we can observe evolutionizing systems and make conclusions about their internal structure from a phenomenological description of how they function.

From the functional point of view the metasystem transition is the case where some activity A, which is characteristic of the top control system of a system S, becomes itself controlled as a metasystem transition from S to S' takes place. Thus the functional aspect of metasystem transitions can be represented by formulas of this kind:

control of A = A'

When a phenomenological description of activities of some systems fits this formula we have all reasons to believe that this is a result of a metasystem transition in the physical structure of the systems. Here is the sequence of metasystem transitions which led, starting from the appearance of organs of motion, to the appearance of human thought and human society:

• control of position = movement
• control of movement = irritability (simple reflex)
• control of irritability = (complex) reflex
• control of reflex = associating (conditional reflex)
• control of associating = human thinking
• control of human thinking = culture
For more details, check "The Quantum of Evolutiuon", a collection of papers on the topic of metasystem transitions, published as a special issue of "World Futures".

# Types of Metasystem Transitions

Author: C. Joslyn,
Updated: Aug 1993
Filename: MSTTYPES.html

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# Freedom and Constraint in a Metasystem Transition

Author: C. Joslyn,
Updated: Jan 1992
Filename: FREECONS.html

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A metasystem transition is fundamentally a process of systems formation. A metasystem transition results in the creation of a new system, and thus new entities which are stable at a higher level of analysis.

Following the metasystem transition the entities (now subsystems) are under the control of the new system. The behavior of the whole is constrained by the parts (a "reductionistic" view), but the behavior of the parts is at the same time constrained by the whole (a "holistic" view). The control of the metasystem decreases the freedom of the subsystems: they are constrained, perhaps not entirely, by the metasystem into certain pathways of activity (see cite{CAD90}).

The new systemic level has its own attributes, and its own variability. Thus the total freedom of the overall metasystem now becomes split into two: that of the parts and that of the whole. How that freedom is distributed is a crucial question, and has some extremes. While the freedom of the subsystems is decreased, the overall freedom and adaptivity of the overall system may increases.

We can identify one limit as an isolated system, in particular an isolated thermodynamic system. As is well known cite{ROW82}, under these conditions the thermodynamic system goes to equilibrium, and the macroscopic properties of the metasystem (pressure, temperature) become completely stable, and show no variation. Simultaneously, according to the second law, the statistical entropy, and thus the variation, of the subsystems (the molecular components) is maximized. Thus under these conditions the entire freedom of the system is "pushed down" to the components: maximal stability of the whole is traded for maximal instability of the parts.

The converse case where the parts become completely constrained occurs in the case of a machine. The structure of a machine constrains its parts along deterministic pathways cite{ASR56}.

# Branching Growth of the Penultimate Level

Author: V. Turchin, F. Heylighen, C. Joslyn,
Updated: Jan 26, 1996
Filename: PENULTIM.html

A fundamental result of a metasystem transition (MST) is that the process of replication of the now subsystems comes under the control of the metasystem. This concentration results in the growth of the penultimate level, or an explosive increase in the number of subsystems embedded in the overall metasystem.

The "law of the branching growth of the penultimate level" might be seen as the beginning of a more detailed dynamics of MST's. It states that after the formation, through variation and selection, of a control system C, controlling a number of subsystems Si, the Si will tend to multiply and differentiate. The reason is that only after the formation of a mechanism controlling the Si it becomes useful to increase the variety of the Si. Complementarily, the larger the variety of Si to be controlled, the more important it is to develop and refine the control mechanism C. The development of C and the multiplication of the Si are thus mutually reinforcing processes. The result is that an MST is characterized by a positive feedback where a small evolutionary change is strongly accelerated, after which it slows down again by the time a new level of equilibrium is reached.

Initially, the integration of replicated subsystems Si can take place only on a small scale. This is the law of combinatorics. The trial and error method can work only on combinations of relatively few elements; otherwise the number of possible combination becomes so huge that there is no chance to find the needle of a stable configuration in this bundle of hay.

However, when the needed combination is found, and a new controlling agent C has emerged, it becomes, typically, possible to control almost any number of integrated subsystems, and this is advantageous for stability because of the geometric and combinatorial factors mentioned above. There starts an integration on a grand scale. The emergent agent is on the ultimate control level of the emergent system; the integrated subsystem make up the penultimate level. A metasystem transition leads to multiplication of these subsystems. When nature discovered the principle of coding protein forms with sequences of four nucleotides, the growth of the number of nucleotides began, resulting in huge molecules with many thousands of nucleotides. When the concept of a cell that can cooperate with other cells emerged, multicellular organisms started being formed with growing numbers of integrated cells, until they reached the sizes of present day animals. The same with human society. A well organized society starts growing exponentially.

# The History of Evolution

Author: F. Heylighen, C. Joslyn, V. Turchin,
Updated: Nov 16, 1994
Filename: HISTEVOL.html

The basic idea underlying the Principia Cybernetica Project is that evolution leads to the spontaneous emergence of systems of higher and higher complexity or "intelligence": from elementary particles, via atoms, molecules, living cells, multicellular organisms, plants, and animals to human beings, culture and society. This gives us a view of the history of evolution as a kind of progression towards higher complexity (albeit essentially unpredictable, with many side-tracks and dead-ends). Such an encompassing view may allow us to answer the basic questions: "Who are we? Where do we come from? Where are we going to?" (The last question requires an extrapolation of this development towards the future.)

Although the growth of complexity during evolution is not universal (many systems evolve towards higher simplicity), it appears as the most striking factor from a long-term perspective. Most of the time this complexity increase, and evolution in general, occurs rather slowly or continuously, but during certain periods evolution accelerates spectacularly. This results in changes which from a long term perspective may be viewed as momentous events, separating discrete types of organization. Each time a higher level of control or organization has developed we say that a Metasystem Transition (MST) has taken place.

The MST concept makes it possible to reconstruct the sequence of evolutionary events from the beginnning of time to the present as a partially ordered series of metasystem transitions. These transitions can be roughly classified in four categories or "tracks":

1. Prebiotic: the developments taking place before the origin of the life, i.e. the emergence of physico-chemical complexity: the Big Bang, space and time, energy and particles, atoms and the different elements, molecules up to organic polymers, simple dissipative structures.
2. Biological: the origin of life and the further development of the specifically biological aspects of it: DNA, reproduction, autopoiesis, prokaryotes vs. eukaryotes, multicellularity, sexual reproduction, the species.
3. Cognitive: the origin of mind, i.e. the basic cybernetic, cognitive organization, going from simple reflexes to complex nervous systems, learning, and thought.
4. Social: the development of social systems and culture: communication, cooperation, moral systems, memes
Although most of the transitions taken place sequentially within each main track, and these track emerge roughly in the order they are presented here, there is also essential interaction between the categories. For example, communication and cooperation between organisms (social track) takes place before rational thought (cognitive track) emerges, and is in a mutual positive feedback relation with that cognitive transition. Similarly, sexual reproduction (biological) appears in parallel with the emergence of reflexes (cognitive) and influences the appearance of social cooperation via its formation of family groupings.

For a chronology of some of these transitions, see the Cosmological and Evolutionary/Geological Timelines.

# Biological Evolution

Author: C. Joslyn, F. Heylighen,
Updated: Aug 14, 1995
Filename: BIOEVOL.html

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The history of biological evolution is marked by a number of metasystem transitions, including: the origin of life itself, the development of the modern cell from the aggregation of pre-cellular organelles (the transition from prokaryotes to eukaryotes); the coordination of single cell organisms into multi-cellular organisms; and the origin of sexual reproduction.

A detailed treatment of these different developments can be found in:

Maynard Smith J. & Szathmàry E. (1995): Major Transitions in Evolution, (W.H. Freeman, Oxford).

From the book cover:

(...). This is the first book on all these major transitions. In discussing such a wide range of topics in one volume, the authors are able to highlight the similarities between different transitions- for example, between the union of replicating molecules to form chromosones and of cells to form multicellular organisms. The authors also show how an understanding of one transition sheds light on others.

A common theme in the book is that entities that could replicate independently before the transition can replicate afterwards only as as part of a larger whole. Why, then, does selection between entities at the lower level not disrupt selection at a higher level? In answering this question, the authors offer an explanation for the evolution of cooperation at all levels of complexity.

# The Origins of Life

Author: C. Joslyn,
Updated: Jan 1992
Filename: ORILIFE.html

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It is rather apparent that the level of the living organism, and in particular the biochemistry of macromolecules like DNA, is the first in evolutionary history to manifest the properties of the semantic coding relationships necessary for anticipatory control. Thus the problem of the origin of the first organism is essentially that of the first metasystem transition leading to an anticipatory control system.

For a control-theoretic scenario for the origin of life, see Powers's paper "The Origins of Purpose"

# Multicellular organisms

Author: D. T. Campbell
Updated: Apr 25, 1995
Filename: MULTICEL.html

Leo W. Buss (1987) in a pioneering monograph has explored the transition from unicellular to multicellular organisms in great detail, and has exemplified how the competition among cells for differential propagation by fission created obstacles to the emergence of multicellular organisms with cellular differentiation (i.e., division of labor). He says:

"The path from a unicellular condition to a multicellular one has been well-traveled. Of the same 23 monophyletic protist groups, fully 17 have multicellular representatives. The path from multicellularity to cellular differentiation, however, proved a far less porous filter. Of the 17 multicellular taxa, only 3 groups-the plants, the fungi, and the animals-have developed cellular differentiation in more than a handful of species. With the evolution of cellular differentiation, kingdoms were made of some protist groups; yet we know virtually nothing as to why this transition was closed to all but a few taxa." (Buss, 1987, p. 70
In agreement with Turchin's definition of Metasystem Transition, Buss portrays the first stage of multicellularity without differentiation of function, except for accidents of location in the adhering mass. With differentiation comes the distinction between germ cells and somatic cells. Somehow the germ cells exchange proliferation by fission within the organism for reproduction by seed across generations, and the specialized somatic cells gain in reproductive opportunities by fission within the organism. It helps in achieving differentiated multicellularity that all of the cells are identical twins in terms of chromosomal genes (implying a shared control). This has not removed the competition among cells for reproduction by fission. Adaptive distribution of such specialized cell proliferation requires additional controls. These are not under any centralized coordination, but are achieved through a widely distributed variety of inducing and inhibiting adjacencies. Keeping these controls tuned so that the integrated division of labor which produces a multicellular-organismic functionality is preserved requires a node of selection at the whole organism level. Such a node was also a prerequisite for its development. This node is implemented by differential propagation of the seeds produced by the germ cells.

Reference:

# Sexuality as a Metasystem Transition

Author: F. Heylighen,
Updated: Jul 26, 1994
Filename: SEX.html

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Variation of DNA can be found in different mechanisms of genetic change: mutations, recombinations, copying errors, ... In order to find a metasystem transition, we need a mechanism that controls or constrains such changes in a non-random way. Although geneticists are still learning a lot about the underlying molecular processes, there is one mechanism which is clearly not the effect of random noise, sexuality. It can be defined as the constrained variation of DNA sequences by recombination with a second sequence from another organism of the same type.

Sexuality makes it possible to increase genetic variety without the dangers of random mutation: since both sequences have proven their viability, is it not very likely that their recombination would be unviable. Mutations, on the other hand, are most likely deleterious. Higher variety (or "diversity" in biological terminology) of offspring implies a lower probability that all of them would be eliminated by natural selection, and a high probability that at least one of them would be more fit thna the parents. The effectiveness of recombination mechanisms for exploring large fitness spaces has been demonstrated in computer applications by genetic algorithms.

The metasystem that emerges from this metasystem transition is the species, which is defined as the set of all organisms that are capable to recombine their genes in the way mentioned. In sexual organisms, no individual organism really reproduces, since the offspring is always genetically different from the parent. The only system that can really be said to maintain and reproduce is the species as a whole, characterized by a more or less stable "gene pool", i.e. the collection of genes available for recombination.

Reference: Heylighen F. (1995): "(Meta)systems as Constraints on Variation: a classification and natural history of metasystem transitions", World Futures: the Journal of General Evolution 45, p. 59-85.

# Cognitive Evolution (stages)

Author: C. Joslyn,
Updated: Jan 1992
Filename: COGNEVOL.html

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The origin of the nerve cell marks another major step in biological evolution. As a communicative device, the function of a nerve cell is to vary so that it can communicate information. Nerve cells can affect a variety of semantic coding relationships, allowing multicellular organisms to rapidly develop many levels of hierarchical anticipatory control. In particular, we recognize in the following schema the major steps of neural evolution:

Culture is control of   thought;
which is	control of  	associating;
which is control of   complex reflexes;
which is control of   simple reflexes;
which is control of   movement;
which is control of   position.


# Human thinking

Author: V. Turchin,
Updated: Oct 24, 1997
Filename: THINKING.html

 Synopsys: Human intelligence, as distinct from the intelligence of non-human animals, results from a metasystem transition that allows the organism to control the formation of associations of mental representations, producing imagination, language, goal setting, humor, arts and sciences.

We still know so little about the process of thinking and the structure of the thinking brain that any theory claiming to explain this phenomenon as a whole is hypothetical. Thus, our conception of thinking must also be treated as a hypothesis. However, this conception indicates the place of thinking in the series of natural phenomena and, as we shall see, puts a vast multitude of facts together in a system. The complete absence of particular, arbitrary assumptions, which ordinarily must be made when a theory includes a structural description of a little-studied object, is another positive feature. The core of our conception is not some hypothesis regarding the concrete structure and working mechanism of the brain, but rather a selection of those functional concepts through which a consistent and sufficiently convincing explanation of the facts we know about thinking becomes possible.

Thus, we assert that the appearance of thinking beings, which marks the beginning of a new stage--perhaps a new era--in evolution (the era of reason) is nothing short of the next metasystem transition, which occurs according to the formula

control of associating = thinking.

To prove this assertion we shall analyze the consequences that follow from control of associating and equate them with the forms of behavior we observe in thinking beings.

First of all, what is control of associating? Representations X and Y are associated in an animal only when they appear together in its experience. If they do not appear together (as a rule, on many occasions), the association will not arise. The animal is not free to control its associations; it has only those which the environment imposes on it. To control associating a mechanism must be present in the brain which makes it possible to associate any two or several representations that have no tendency at all to be encountered together in experience--in other words, an arbitrary association not imposed by the environment.

This action would appear to be completely meaningless. An elder tree in the garden and an uncle in Kiev--why connect these two totally unrelated facts? Nonetheless, arbitrary associating has profound meaning. It really would be meaningless if brain activity amounted to nothing more than passively receiving impressions, sorting them, grouping them, and so on. But the brain also has another function--its basic one: to control the organism, carrying out active behavior which changes the environment and creates new experience. You can bet that the alarm clock and the holder for the teapot are in no way associated in your consciousness. Nor in the consciousness of your three-year-old son. However, this is only for a certain time. One fine day, for some reason an association between these two objects occurs in the head of the young citizen and he is overcome by an insurmountable desire to rap the alarm clock with the holder. As a result, the objects enter a state of real, physical interaction.

In the metasystem transition, some thing that was once fixed and uniquely determined by external conditions becomes variable and subject to the action of the trial and error method. Control of associating, like every metasystem transition, is a revolutionary step of the highest order directed against slavish obedience by the organism to environmental dictatorship. As is always true in the trial and error method , only a small proportion of the arbitrary associations prove useful and are reinforced, but these are associations which could not have arisen directly under the influence of the environment. And they are what permits a reasoning being those forms of behavior which are inaccessible to the animal that was frozen in the preceding stage.

Reference:
Turchin V. (1977): The Phenomenon of Science. A cybernetic approach to human evolution, (Columbia University Press, New York).

# Social Evolution

Author: C. Joslyn, V. Turchin, F. Heylighen,
Updated: Aug 9, 1995
Filename: SOCEVOL.html

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We have loosely used the informal concept of the "organism" as a distinct kind of living entity. We must recognize that this distinction is largely arbitrary, and only useful in limited domains. A number of examples, such as viruses, beehives, slime molds, and other symbiotic and parasitic relationships threaten to blur the distinction between the living organism and the community as the elemental living system.

Yet if we assume the existence of distinct organisms, then the hierarchy of neural evolution is marked by a series of metasystem transitions within organisms. But at the same time, metasystem transitions also occur which bring organisms together in groups. Some simple examples include breeding populations and the dynamics of fish schools and bird flocks. When these group controls are very strong, some of the more marked transitions (e.g. the development of multicellular organisms) result.

But when the group controls are considerably weaker, we have the existence of societies of organisms. The most integrated form of such societies can be found in the social insects: ants, bees and termites. Human society is much less strongly integrated but much more complex. Higher-level societies are usually marked by culture, which can be defined simply as models which are inherited between organisms in a non-genetic manner. We can define such non-genetic information, when carried between people, as memes. Memes, similar to genes, undergo a variation and selection type of evolution, characterized by mutations and recombinations of ideas, and by their spreading and selective reproduction or retention.

As outlined in our discussion of cognitive evolution, thought can be understood as the ability to control the production, reproduction and association of memes in the minds of humans. What follows is the possibility of evolution at the memetic level. The emergence of human thought marks the appearance of a new mechanism of evolution: conscious human effort instead of natural selection. The variation and selection necessary for the increase of complexity of the organization of matter now takes place in the human brain; it becomes inseparable from the willed act of the human being.

Thus the emergence of human intelligence and memetic evolution precipitated a further, currently ongoing, Metasystem Transition, which is the integration of people into human societies. Human societies are qualitatively different from societies of animals because of the ability of the human being to create (not just use) language. Language serves two functions: communication between individuals and modeling of reality. These two functions are, on the level of social integration, analogous to those of the nervous system on the level of integration of cells into a multicellular organism. The body of a society is the bodies of all people plus the things made by them. Its "physiology" is the culture of society.

Using the material of language, people make new --- symbolic --- models of reality (scientific theories, in particular) such as never existed as neural models given us by nature. Language is, as it were, an extension of the human brain. Moreover, it is a unitary common extension of the brains of all members of society. It is a collective model of reality that all members of society labor to improve, and one that preserves the experience of preceding generations.

# Insect Societies

Author: D.T. Campbell
Updated: Aug 9, 1995
Filename: INSECSOC.html

The integrated division of labor within the insect colony (with the various castes of workers and soldiers) has been achieved by selection of single queens. (In later evolution, the nest acquires such strong entitativity that queen replacement and even multiple queens can occur.) In the ants, the probable initial stage was brood-care help by daughters with postponed fertility. This created an ecology in which only nests with such auxiliary helpers survived. Nest (or colony) selection began at this point. Postponed fertility was probably augmented by a pheromone exuded by the fertile mother.

Initially there was no division of labor or caste specialization. Mother and infertile daughters were all capable of all tasks of food-gathering and brood-care. While it was in the inclusive-fitness interests of each of the sterile workers to become fertile, it was also in the inclusive-fitness interests of each to keep her sisters sterile, as by distributing the queen's fertility-inhibiting pheromone to her sisters (including those in larval stages) and by eating the haploid drone eggs that some of her supposedly sterile sisters might produce. Once worker sterility was dependably achieved, then selection by colony could dominate because the individual-vs.-individual genetic competition had been almost completely eliminated. From this point on, the elaborate division of labor into the several worker and soldier castes could develop (Wilson, 1971; Campbell, 1983).

Note that in none of the social insects are the sterile workers and/or soldiers genetically identical, nor do the castes differ systematically in their genetic composition. The differentiation into worker, soldier, and queen is achieved by differential feeding and pheromone exposure in early development. Note, too, that in the social insects the genetic competition among the cooperators has not been entirely suppressed, and shows itself in many minor ways such as kin-favoritism of workers in caring for larvae, and among the bees, at least, when the queen is removed, disruptive genetic competition among the disinhibited formerly-sterile workers (Seeley, 1985, 1989; Winston and Slessor, 1992).

The anatomical differentiation among the castes is controlled by distributed (rather than centralized) inhibitions. So, too, the communication and coordination among the castes. Some dozen different pheromones are involved, all released and distributed in automatic reflex ways. Quasi-entropic effects of mutations produce discoordination. Again, it is selection by whole nest survival that keeps these dozens of adjustments tuned for whole-nest functionality. Unique nest odors prevent individuals from transferring membership from nest to nest, further ensuring whole-nest selection.

Reference: Heylighen F. & Campbell D.T. (1995): "Selection of Organization at the Social Level: obstacles and facilitators of metasystem transitions ", World Futures: the Journal of General Evolution 45, p. 181-212.

# Human society

Author: D.T. Campbell, F. Heylighen,
Updated: Aug 9, 1995
Filename: SOCIETY.html

Humans form effective, coordinated, division-of-labor groupings at several levels of aggregation. At each level, there is a problem of metasystem transition. At each level, there is not only competition between other groupings at the same level, but also competition between the interests of the smaller incorporated units and the interests of the larger encompassing unit. Primary, face-to-face, groups are incorporated into organized city-states, and these into nations. A plausible node of selection and inter-organization competition can be envisaged at each of these levels. The great majority of evolutionary biologists deny the efficacy of biological group selection of those "altruistic" traits in which individuals act for the preservation of the group at the risk of their own well being and "inclusive fitness" (i.e., the representation of their own genes in future generations). This is not to deny the occurrence of group selection, but rather to say that its effects for self-sacrificial altruistic traits will be undermined by individual-vs.-individual selection. A group with heroically self-sacrificing altruists may thrive better. The inclusive fitness gains from this will be shared equally by the non-altruists within the group. For the altruists, these gains are in part undermined by the risks they run. The non-altruists pay no such costs, and thus out-breed the self-sacrificial altruists in the within-group genetic competition. (For the soldiers, etc., of the social insects, this intra-social-organization genetic competition has been eliminated by the sterility of each of the cooperating castes).

Our previous position accepted the following:

1. Individual selection always dominates group selection at the biological level;
2. "Groups are real" (Campbell, 1958) as opposed to methodological individualism;
3. Self-sacrificial altruism in the service of human social groups genuinely exists;
4. Such altruism can only be produced by group selection.
The solution was to limit group selection to non-biological cultural evolution and to see self-sacrificial altruism as a result of cultural group selection of ideologies, social-organizational traditions, moral indoctrination, and religious cosmologies (Campbell, 1972, 1975, 1983, 1991; Heylighen, 1992a, 1992b). This point of view had many plausible implications, among them an explanation of why moral commandments and lists of deadly sins contain explicit rejections of innate human nature. There is also the obvious group-coordination utility of beliefs in rewarding and punishing afterlives and reincarnations, which extend perceived self-interest into an afterlife and thus can promote self-sacrificial acts.

This simple point of view we are now ready to substantially modify for social control mechanisms within primary groups, retaining its relevance for secondary groups. One influence is the increased plausibility of biological group selection as seen by evolutionary biologists (cf. Wilson and Sober, 1994). All along, biological evolution has been credited for the human capacity for culture, including competent communication of useful information between individuals. But even in much less social animals, social communication creates a niche for self-serving deception, and biological group selection may be needed to keep the rate of such parasitism low enough so that there is a net collective communicative advantage. The resulting proximal mechanisms would include mutual monitoring and retaliation for "immoral" behavior (an analogue for the mutual enforcement of sterility among the social insect castes). We humans probably have an innate fear of ostracism, and a tendency to find painful the signs of hostility on the part of those we work or live with on a regular face-to-face basis. Innate tendencies to enforce group solidarity on others would be supported by both individual and group selection and may be identified as prerequisite for group selection.

The route to a cultural-evolutionary group selection that had been proposed contained several stages that built upon biologically evolved bases (Boyd and Richerson, 1985; Campbell, 1983, 1991). While these are presented as advantageous at the individual selection level, they are both plausible routes to biological group selection and might require group selection to avoid free rider parasitism and elimination by the negative costs of the self-sacrificial altruism they produce:

1. An innate tendency to "conformist transmission" (Boyd and Richerson, 1985) would be individually adaptive for skills and environmental wisdom, but would also lead to primary group homogeneity on neutrally adaptive beliefs and behaviors. (In analogy with the role of "genetic drift" in biological evolution, this can be called "memetic drift.") These ingroup homogeneities and chance-produced intergroup differences provide the necessary setting for group selection if some of these chance homogeneities produced superior group-vs.-group competition.
2. Trivers (1971) has posited an individually adaptive innate predisposition to joining reciprocally altruistic cliques, and a related innate tendency for "moralistic aggression" when such pacts are violated. (The latter might turn out to require group selection to supplement individual selection.) As we have noted above, this pattern may be summarized as "clique selfishness." It would be individually adaptive to join already existing selfish cliques. Culturally transmitted ingroup membership may be regarded as providing such opportunities (Brewer, 1981). Along with this individually adaptive cultural scaffolding innate predispositions might be selected. These would include a tendency to join and conform to such cliques, and also to pressure one's biological offspring to conform.
3. Effective ingroup or selfish clique membership is furthered by visible and audible clues to ingroup membership. The neutrally adaptive ingroup homogeneities produced by conformist transmission would be available for such use. This would further sharpen the ingroup homogeneities and intergroup heterogeneities necessary for group selection at the cultural or biological level
. As we (e.g. Campbell and Gatewood, 1994) understand their argument, Wilson and Sober (1994) propose that group selection and individual selection can be concurrent, producing an ambivalence on the group preservation vs. individual preservation dimension. In behavioral evolutionary jargon, this would be a "facultative polymorphism." (For example, the males in many species of monkeys have two incompatible innate behavioral repertoires, one for submission, one for dominance. The learned dominance rank determines which will be displayed in which encounter.)

Even if biological group selection has occurred in human evolution, the persistence of genetic competition among the cooperators has produced a profoundly ambivalent social animal, in sharp contrast with the sterile castes of the social insects. For humans in social organizations, organizational optimizing is in continuous conflict with optimizing individual well-being and inclusive fitness. In parallel, primary group social solidarity competes with secondary group optimization in industrial and governmental bureaucracies.

Reference: Heylighen F. & Campbell D.T. (1995): "Selection of Organization at the Social Level: obstacles and facilitators of metasystem transitions ", World Futures: the Journal of General Evolution 45, p. 181-212.

# Memetics

Author: F. Heylighen,
Updated: Jan 28, 1998
Filename: MEMES.html

Synopsys:
 Meme: an information pattern, held in an individual's memory, which is capable of being copied to another individual's memory. Memetics: the theoretical and empirical science that studies the replication, spread and evolution of memes

Cultural evolution, including the evolution of knowledge, can be modelled through the same basic principles of variation and selection that underly biological evolution. This implies a shift from genes as units of biological information to a new type of units of cultural information: memes.

A meme is a cognitive or behavioral pattern that can be transmitted from one individual to another one. Since the individual who transmitted the meme will continue to carry it, the transmission can be interpreted as a replication: a copy of the meme is made in the memory of another individual, making him or her into a carrier of the meme. This process of self-reproduction, leading to spreading over a growing group of individuals, defines the meme as a replicator, similar in that respect to the gene (Dawkins, 1976; Moritz, 1991).

Dawkins listed the following three characteristics for any successful replicator:

copying-fidelity:
the more faithful the copy, the more will remain of the initial pattern after several rounds of copying. If a painting is reproduced by making photocopies from photocopies, the underlying pattern will quickly become unrecognizable.
fecundity:
the faster the rate of copying, the more the replicator will spread. An industrial printing press can churn out many more copies of a text than an office copying machine.
longevity:
the longer any instance of the replicating pattern survives, the more copies can be made of it. A drawing made by etching lines in the sand is likely to be erased before anybody could have photographed or otherwise reproduced it.

In these general characteristics, memes are similar to genes and to other replicators, such as computer viruses or crystals. The genetic metaphor for cultural transmission is limited, though. Genes can only be transmitted from parent to child ("vertical transmission"). Memes can be transmitted between any two individuals ("horizontal transmission" or "multiple parenting"). In that sense they are more similar to parasites or infections (cf. Cullen, 1998).

For genes to be transmitted, you need a generation. Memes only take minutes to replicate, and thus have potentially much higher fecundity (see Competition between Memes and Genes). On the other hand, the copying-fidelity of memes is in general much lower. If a story is spread by being told from person to person, the final version will be very different from the original one. It is this variability or fuzziness that perhaps distinguishes cultural patterns most strikingly from DNA structures: every individual's version of an idea or belief will be in some respect different from the others'. That makes it difficult to analyse or delimit memes. This does not imply that meme evolution cannot be accurately modeled, though. After all, genetics was a well-established science long before the precise DNA structure of genes was discovered.

Examples of memes in the animal world are most bird songs, and certain techniques for hunting or using tools that are passed from parents or the social group to the youngsters (Bonner, 1980). In human society, almost any cultural entity can be seen as a meme: religions, language, fashions, songs, techniques, scientific theories and concepts, conventions, traditions, etc. The defining characteristic of memes as informational patterns, is that they can be replicated in unlimited amounts by communication between individuals, independently of any replication at the level of the genes.

Of course, the capacity of the nervous system for learning is the result of evolutionary processes at the genetic level. Yet I will here not go into detail about why that capacity has been selected. The increased fitness resulting from a nervous system that is flexible enough to adapt its behavior to many new situations, seems obvious enough. If a useful type of behavior can be learned directly from another individual by communication or imitation, that seems like a most welcome shortcut for having to discover it by personal trial-and-error. More arguments for why the capacity for meme replication has evolved genetically can be found in most texts about the recently founded domain of memetics (Moritz, 1991).

Memetics can be defined as an approach trying to model the evolution of memes (see e.g. Boyd & Richerson, 1985; Cavalli-Sforza & Feldman, 1981; Lumsden & Wilson, 1981; Csanyi, 1991; Lynch, 1998). Memes undergo processes of variation (mutation, recombination) of their internal structure. Different variants will compete for the limited memory space available in different individuals. The most fit variants will win this competition, and spread most extensively. Different criteria for the fitness of a meme, relative to other memes, can be formulated.

Variation, replication and selection on the basis of meme fitness determine a complex dynamics. This dynamics will be influenced by the medium through which memes are communicated, and the copying-fidelity, fecundity and longevity it allows. Perhaps the most powerful medium for meme transmission is the computer network, and this implies some specific characteristics for memes on the net.

As is the case with genes, it is not necessary to know the exact coding or even the exact size or boundaries of a meme in order to discuss its fitness, and thus to make predictions about its further spreading, survival or extinction within the population of competing memes. Such predictions can be empirically tested. For example, a memetic hypothesis might state that simpler memes will spread more quickly. This can be tested by observing the spread (perhaps in a controlled environment) of two memes that are similar in all respects, except that the one is simpler. Theories can also be induced from empirical observation of meme behavior "in the wild" (see e.g. Best, 1998). Given the differences in variation and selection mechanisms, it is also possible to make predictions about the competition between memes and genes.

## References:

• Best, M., L., 1997; Models for Interacting Populations of Memes: Competition and Niche Behavior. Journal of Memetics - Evolutionary Models of Information Transmission, 1.
• Bonner J.T. (1980): The Evolution of Culture in Animals, (Princeton University Press, Princeton).
• Boyd R. & Richerson P.J. (1985): Culture and the Evolutionary Process, (Chicago University Press, Chicago).
• Cavalli-Sforza L.L. & Feldman M.W. (1981): Cultural Transmission and Evolution: a quantitative approach, (Princeton University Press, Princeton).
• Csanyi V. (1991): Evolutionary Systems and Society: a general theory, (Duke University Press, Durham, NC).
• Cullen B. (1998): "Parasite Ecology and the Evolution of Religion", in: Heylighen F. (ed.) (1998): The Evolution of Complexity (Kluwer Academic, Dordrecht)
• Dawkins R. (1976): The Selfish Gene, (Oxford University Press, New York).
• Heylighen F. (1992) : "Selfish Memes and the Evolution of Cooperation", Journal of Ideas , Vol. 2, #4, pp 77-84.
• Lynch, Aaron (1997) "Units, Events, and Dynamics in Memetic Evolution", Journal of Memetics (submitted)
• Lumsden, Charles, and Wilson, Edward (1981): Genes, Mind, and Culture: the Coevolutionary Process, (Harvard University Press, Cambridge).
• Moritz E. (1990): "Memetic Science: I - General Introduction", Journal of Ideas 1, p. 1-23
• Moritz E. (1995): Metasystems, Memes and Cybernetic Immortality, in: Heylighen F., Joslyn C. & Turchin V. (eds.), The Quantum of Evolution. Toward a theory of metasystem transitions, (Gordon and Breach Science Publishers, New York) (special issue of "World Futures: the journal of general evolution, vol. 45, p. 155-171).

# Structure of memes

Author: F. Heylighen,
Updated: Aug 18, 1994
Filename: MEMSTRUC.html

## Modelling meme units

The main criticism that can be raised against the memetic approach is that memes are difficult to define. What are the elements or units that make up a meme? Does a meme correspond to a complete symphony, or to a symphonic movement, a melody, a musical phrase, or even a single note?

In order to model meme structure, we may use some concepts from cognitive science. Perhaps the most popular unit used to represent knowledge in artificial intelligence is the production rule. It has the form "if condition, then action". In symbols:

If A, then B or A -> B

A represents a condition that is distinguished, B represents an action that is executed or another condition that is activated. The action leads in general to the activation of another condition. In fact a production rule can be analysed as a combination of even more primitive elements: two distinctions (which discriminate between presence and absence of the condition and the action respectively) and a connection (the "then" part, which makes the first distinction entail the second one) (Heylighen, 1991d; see also Heylighen, 1990). For example, a meme like "God is omnipotent" can be modelled as "if a phenomenon is God (distinction of God from non-God), then that phenomenon is omnipotent".

Production rules are connected when the output condition (action) of the one matches the input condition of the other. E.g. A -> B, B -> C. This makes it possible to construct complex cognitive systems on the basis of elementary rules. Even remembered melodies might be modelled in such a way, as concatenations of production rules of the type "if C (musical note distinguished), then E (note produced and subsequently distinguished)", "if E, then A", and so on.

A similar model applies to genes. A gene corresponds to a string of DNA codons, which respond to the presence of certain activating proteins, or the absence of certain inhibiting proteins (condition) by manufacturing new proteins (action). This may in turn activate further genes, depending on the present of specific chemicals in the cell, and so on. This leads to complex networks of "if... then" productions ( Kauffmann, 1992).

## Variation of memetic units

It has been shown that production rules (or at least a simplified, binary representation of them, called "classifiers") can be used to build quite impressive computer simulations of cognitive evolution, using mutations, recombinations, and selection on the basis of "fitness" (Holland et al., 1986).

Distinctions can be represented as combinations (strings) of elementary yes-no (1-0) observables. Mutation or recombination of distinctions can then be modelled by either randomly changing certain binary digits in a string, or by concatenating the first part of one string (A) with the second part of another string (B), like in the following example:

  A = 1001|001     mutation: A' = 1001|000
B = 0010|011    recombination ("crossing-over") of A and B:
A~B = 1000|011
`

Although these models do not as yet take into account distinct carriers, this looks like a very promising road to study memes formally and computationally.

## Meme complexes

Even if we would model memes as connected sets of production rules, we still have the problem of how many production rules define a single meme. If we call a religion or a scientific theory a meme, it is clear that this will encompass a very large number of interconnected rules. In practice it will be impossible to enumerate all rules, or to define sharp boundaries between the rules that belong to the meme and those that do not. However, that should not detract us from using memetic mechanisms in analysing evolution.

Indeed, Darwinian models of genetic evolution have certainly proven their usefulness, even though it is in practice impossible to specify the exact DNA codons that determine the gene for, say, blue eyes or altruism towards siblings. As Dawkins (1976) notes, it is not necessary to be explicit about what are the constitutive elements of a gene, postulated to explain a particular characteristic or type of behavior. It is sufficient that we can distinguish the phenotypical effects of that gene from the effects of its rival genes (alleles). If we can determine the fitness resulting from these effects, taking into account the environment and the context of different, non-rival genes present in the genome, then we can make predictions about evolution.

The same applies to memes. If, for example, we observe that one meme (say Catholicism) induces its carriers to have more children than its competitors (say Calvinism and Anglicanism), and that the children tend to take over their memes from their parents, then, all other things being equal, we can predict that after sufficient time that meme will dominate in the population. Of course, in practice it is never the case that all other things are equal, but that is the predicament of all scientific modelling: we must always simplify, and ignore potentially important influences. The question is to do that as wisely as possible, and to maximally include relevant variables without making the model too complex.

## References

• Dawkins R. (1976): The Selfish Gene, (Oxford University Press, New York).
• Heylighen F. (1992) : "Selfish Memes and the Evolution of Cooperation", Journal of Ideas , Vol. 2, #4, pp 77-84.
• Holland J.H., Holyoak K.J., Nisbett R.E. & Thagard P.R. (1986): Induction : processes of inference, learning and discovery, (MIT Press, Massachusetts).
• Kauffman S.A. (1992): Origins of Order: self-organization and selection in evolution, (Oxford University Press, Oxford).

# Memetic Selection Criteria

Author: F. Heylighen,
Updated: Sep 5, 1995
Filename: MEMSELC.html

These are the criteria that determine the overall fitness of a meme, i.e. whether it will maintain within an individual's memory and spread to other individuals, or be eliminated (Heylighen, 1992, 1993). As meme spreading depends on different objective, subjective and intersubjective mechanisms, the criteria are sometimes contradictory. See also the general selection criteria for knowledge.

#### * Contribution to individual fitness

A fit meme should help its carrier to survive and reproduce. That means the meme should not induce behaviors that are useless (wasting resources) or dangerous.

#### * Reliability of Predictions

Useful behaviors imply correct anticipations of the effect of actions. Memes producing predictions that turn out to be wrong will tend to be eliminated.

#### * Learnability

A meme should be easily assimilated to the cognitive system. This implies that it should not be too complex, and should not too directly contradict already established rules (coherency), which may be genetic or memetic of origin. In particular it means that rules that are consonant with genetic injunctions will be much easier to learn.

#### * Ease of communication

Memes that are easily transmitted to another individual, either because they lead to a salient behavior that is easy to imitate, or can be clearly expressed in language or other media, will have a higher fitness.

#### * Tendency to be transmitted

Memes that induce their carriers to actively "convert" or "teach" other individuals, thus stimulating their transmission, will be more fit.

#### * Conformity pressure = "Meme selfishness"

As memory space is limited and cognitive dissonance tends to be avoided, it is difficult for inconsistent memes to have the same carriers. Cognitively dissonant memes are in a similar relation of competition as alleles: genes that compete for the same location in the genome. Memes that induce behavior in their carriers that tends to eliminate rival memes will be more fit, since they will have more resources for themselves. The concrete result is that a group of carriers with different memes will tend towards homogeneity, resulting from the imposition of the majority meme and elimination of all non-conforming memes (Boyd & Richerson, 1985).

#### * Contribution to collective fitness

Memes that increase the fitness of the group or social system formed by their carriers are more likely to get more carriers, because succesful groups expand or are imitated. Collective fitness is sometimes in contradiction with individual fitness because of the problem of suboptimization: what is best for an individual is not always best for the group.

# Competition between Memes and Genes

Author: F. Heylighen,
Updated: Aug 18, 1994
Filename: MEMGEN.html

## Different mechanisms of evolution

Though memetic and genetic evolution are subjected to the same basic principles of blind variation and natural selection on the basis of fitness, memetic evolution is basically a much more flexible mechanism. Genes can only be transmitted from parents (or parent in the case of asexual reproduction) to offspring. Memes can in principle be transmitted between any two individuals (though it will become more difficult the larger the differences in cognitive mechanisms and language are). (this is sometimes called "multiple parenting").

For genes to be transmitted, you typically need one generation, which for higher organism means several years. Memes can be transmitted in the space of hours. Meme spreading is also much faster than gene spreading, because gene replication is restricted by the rather small number of offspring a single parent can have, whereas the amount of individuals that can take over a meme from a single individual is almost unlimited. Moreover, it seems much easier for memes to undergo variation, since the information in the nervous system is more plastic than that in the DNA, and since individuals can come into contact with much more different sources of novel memes. On the other hand, selection processes can be more efficient because of "vicarious" selection (Campbell, 1974): the meme carrier himself does not need to be killed in order to eliminate an inadequate meme; it can suffice that he witnesses or hears about the troubles of another individual due to that same meme.

The conclusion is that memetic evolution will be several orders of magnitude faster and more efficient than genetic evolution. It should not surprise us then that during the last ten thousand years, humans have almost not changed on the genetic level, whereas their culture (i.e. the total set of memes) has undergone the most radical developments. In practice the superior "evolvability" of memes would also mean that in cases where genetic and memetic replicators are in competition, we would expect the memes to win in the long term, even though the genes would start with the advantage of a well-established, stable structure. This explains why sociobiological models of human behavior can only be partially correct, as they neglect memetic factors.

## Different selection criteria

When memetic and genetic fitness criteria are inconsistent, the different implicit objectives of memes and genes will lead to a direct competition for control of the carrier's behavior. Both replicators have similar aims to the degree that they use the same vehicles: individual organisms. Everything that strengthens the vehicles should in general be good for the replicators, and hence both genes and memes should be selected on the basis of their support for increased survivability and reproducability of their carriers. However, the implicit goals of genes and memes are different to the degree that they use different mechanisms for spreading from one vehicle to another one. Memes will be positively selected mainly for increased communicability. Genes will be selected mainly for sexual reproducability. These different emphases may lead to direct conflicts.

For example, priests in many religions are prohibited to marry and to have children, in striking disagreement with genetic injunctions. Yet we can easily imagine that the religious meme of celibacy would have been selected because unmarried priests can spend more time and energy on "spreading the word", and hence replicating the meme.

An even more vivid example of countergenetic behavior, closely related to the issue of altruism, is that of martyrs, suicide teams, or kamikaze pilots, who are willing to give up their life in order to promote the spread of a meme: a religion, an ideology or a nation (i.e. a group defined by a common culture or ideology). In that case, the loss of one or a few carriers is compensated by the increased chances of survival for the other carriers or for the meme itself. For example, the suicide of an individual may attract the attention of other individuals to the meme he is carrying, and thus facilitate its spreading. A well-known example is Jan Palach, the Czech student who put himself to fire in order to protest the Soviet suppression of the "Prague Spring". In this case the meme would be the Czech version of "socialism with a human face".

Reference: Heylighen F. (1992) : "Selfish Memes and the Evolution of Cooperation", Journal of Ideas , Vol. 2, #4, pp 77-84.

# Memes on the Net

Author: F. Heyilghen
Updated: Oct 3, 1997
Filename: MEMENET.html

It is obvious that the media by which a meme is communicated, such as scientific journals, church preachings, or radio stations, will greatly influence its eventual spread. The most important medium at present is the emerging global computer network, which can transmit any type of information to practically any place on the planet, in a negligible time.

This highly increased efficiency of transmission directly affects the dynamics of replication. Meme transmission over the network has a much higher copying-fidelity than communication through image, sound or word. Digitalisation allows the transfer of information without loss, unlike the analog mechanisms of photocopying, filming or tape recording. Fecundity too is greatly increased, since computers can produce thousands of copies of a message in very little time. Longevity, finally, becomes potentially larger, since information can be stored indefinitely on disks or in archives. Together, these three properties ensure that memes can replicate much more efficiently via the networks. This makes the corresponding memotypes and sociotypes potentially less fuzzy.

In addition, the network transcends geographical and cultural boundaries. This means that a new development does not need to diffuse gradually from a center outwards, as, e.g., fashions or rumours do. Such diffusion can easily be stopped by different kinds of physical or linguistic barriers. On the net, an idea can appear virtually simultaneously in different parts of the world, and spread independently of the distance or proximity between senders and receivers.

## Chain letters

The simplest example of a meme that takes into advantage these network features is a chain-letter: a message sent to different people with the express request to copy it and distribute it further. This is motivated by anticipated rewards for those who do (and punishment for those who don't). Paper chain-letters are often poorly readable photocopies, or manuscripts retranscribed numerous times by hand or by typewriter, with the insertion of plenty of spelling and semantic errors. The effort and cost of copying and distribution moreover limit the number of copies per generation to about 20. Chain-letters distributed by electronic mail, on the other hand, can be sent to hundreds or thousands of people at once, at virtually no efforts or costs, and without information degradation.

Though I have received more chain-letters by email than by post, chain-letters on the net are still a minor phenomenon. Although their spread is very much facilitated by the net, the same applies to all other types of messages. That means that there is increased competition between all these different memes for a limited resource: the attention a user pays to the information he or she receives. Because chain-letters fulfil relatively few of the criteria that distinguish successful memes from unsuccessful ones, they are unlikely to win this competition.

## Virtual replication on the Web

The recent development from the net as carrier of email messages to the World-Wide Web as repository of interconnected documents has greatly changed the dynamics of meme replication. On the Web, information is no longer distributed by sending copies of files to different recipients. The information is rather stored in one particular location, the "server", where everyone can consult it. "Consultation" means that a temporary copy of the file is downloaded to the RAM memory of the user's computer, so that it can be viewed on the screen. That copy is erased as soon the user moves on to other documents. There is no need to store a permanent copy since the original will always be available. That does not mean that replicator dynamics no longer apply: the interested user will normally create a "bookmark" or "link", i.e. a pointer with the address of the original file, so that it can be easily retrieved later. A link functions as a virtual copy (also called an "alias" file), which produces real, but temporary, copies the moment it is activated.

The success of a web document can then be measured by the number of virtual copies or links pointing to it: the documents with most pointers will be used most extensively. There are already web robots, i.e. programs which automatically scan the Web, that make "hit parades" of the documents which are linked to most often. For example, it is likely that a reproduction of the works of Van Gogh on the Web will be much more popular in number of pointers than the work of some unknown 20th century painter.

## Meme Cooperation: towards a Global Brain

Let us now see how memes on the net can cooperate or compete. Like genes, memes on the web are arranged in networks, where one document points to a number of supporting documents, which in turn link to further supporting documents. Linked documents cooperate, in the sense that they support, confirm or extend each other's ideas. Competing documents, such as announcements of commercial competitors, will not link to each other, or only refer to each other with a phrase like "you should certainly not believe what is said there".

Assuming that two competing documents are equally convincing otherwise, the competition will be won or lost by the number of links that point to each of them. The more pointers to a document can be found, the more people will consult it, and the more further pointers will be made. This is the same kind of self-reinforcing process that leads to conformity, to all members of a group settling on the same meme ensemble. The difference is that now there are no separate groups: on the global network, everyone can communicate with everyone, and every document can link to every other document. The end result is likely to be the emergence of a globally shared ideology, or "world culture", transcending the old geographical, political and religious boundaries. (Note that such homogeneization of memes only results for memes that are otherwise equivalent, such as conventions, standards or codes. Beliefs differing on the other dimensions of meme selection will be much less influenced by conformist selection.)

Such a networked ideology would play a role similar to that of the genome, the network of interconnected genes that stores the blueprint, and controls the physiology, of a multicellular organism. The corresponding "organism" or sociotype for this meme network would be the whole of humanity, together with its supporting technology. Individual humans would play a role similar to the organism's cells, which in principle have access to the whole of the genome, but which in practice only use that part of it necessary to fulfil their specific function.

There is a better metaphor for the emerging global network. Rather than comparing it to an organism's genome, which is normally static and evolves only because of random copying errors, it can be likened to the organism's brain, which learns and develops in a non-random way. The network functions like a