Introduction to Neocybernetics
There are (more or less) eternal mysteries facing us:
- How to reach emergent coordination in agent systems?
- How to explain biodiversity in nature?
- How to characterize robustness in natural networks?
- How to understand the "whirls" in the flow of entropy?
- How to define life?
Read on — this is not only nonsense. The new approach to complex systems, or neocybernetics, seems to give new intuitions into these all-embracing issues.
Cybernetic systems are complex systems with emergent behaviors. They are characterized by mutual interactions and feedbacks among lower-level actors, resulting in dynamic structures. The emergence in such systems is manifested as self-organization and self-regulation.
The key point in neocybernetics is that the emergent models are studied directly rather than the physical first-principles ones. It turns out that the analysis and synthesis tools are based on multivariate statistical methods.
It has been assumed that behaviors in complex systems cannot be analyzed using traditional mathematics: Deeply nonlinear and chaotic models are needed. However, when one directly concentrates on the final patterns, counterintuitively, one can trust on linearity and stability. Rather than studying the hopelessly complex nonlinear iterations at the edge of chaos - the mainstream approach today - much broader views can be seen around the dynamic equilibria.
It turns out that cybernetic systems are based on higher-order balances. Surprisingly, it is control theory that offers the conceptual tools for understanding the behaviors in nature: A cybernetic system implements model-based control of its environment.
Next: The Rules
If you are interested, please see the Presentations and Publications.