Definition of the domain

- the transdisciplinary study of the self-organization, evolution and interaction of complex systems, - applied to the design of support systems for solving complex problems

A dynamical approach to complexity

The basic subject of the proposed research would be complexity, not so much as a static property of certain systems, but as the result and the driving force of evolution and change. The difficulty in interacting with complex systems does not reside in their intrinsic structure, but in the unpredictability, ambiguity and uncontrollability which derive from it. On the other hand, it is this same unpredictability which may give rise to the emergence of qualitatively new, unexpected phenomena, i.e. to creative change.

The unity of theory and practice

In the study of complex evolution roughly two domains can be distinguished: Ą the spontaneous, "natural" evolution of existing complex systems (physical and chemical systems, organisms, societies, ecologies, ...);

Ą the planned or willed interaction with complex systems (adaptation to a complex environment, analysis, management and design of complex systems,...).

The first domain may be characterized as the general study of self-organization [i.e. the spontaneous emergence of organized (=complex) systems], and would fall under what is called fundamental research, characterized by theoretical analysis and empirical observation.

The second domain may be characterized as the study of complex problem-solving, and would fall under applied research, characterized by the design of tools and technologies, and the undertaking of research-based practical action. We contend that both approaches to the study of complexity must work closely together, so that the one may provide ideas and feedback for the other one.

Transdisciplinary integration

Complexity is a subject which cannot be restricted to one or a few disciplines. Complexity exists in any and all domains: physical, chemical, biological, psychological, social, economical, ... In many existing disciplines useful methods for modelling complexity have evolved. Some of the more specific approaches include: cybernetics and systems theory, theories of self-organization in thermodynamics and biology, neural networks, cognitive science, artificial intelligence, computer science, the mathematics of non-linearity, chaos and fractals, fuzzy systems, management and organization science, andragology, ...

It is clear that a profound, global study of the problem of complexity will demand an integration of a maximum of these existing ideas, irrespective of the original context from which they emerged, i.e. a transdisciplinary approach.