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.

Copyright© 1995 Principia Cybernetica -
Referencing this page