chaos & dynamical systems theory

Illustration of a Julia Set by Scott Hotton. Dynamical Systems Theory (a branch of mathematics used to describe the behavior of complex systems by employing differential and difference equations) is another limited framework for modeling complex systems. More accurate than linear and non-linear models, but none-the-less reductionist. (Well, talk about restating the obvious when it comes to anything mathematical, as the concept itself is a reduced language for expressing natural phenomena — I don’t subscribe to the early Greek concept where mathematics does not represent but is a universal and perfect thing unto itself). While human-generated system solutions (say, engineering problems such as placing satellites into orbit) are solved through classic computational modeling with linear systems, natural systems like the brain need something more.

Chaotic systems are especially sensitive to initial conditions. initial conditions are necessary for any reductive system analysis because in the abstraction process of reduction, the system is extracted and disconnected from the continuum of life. Good for mathematical (computational) modeling. But when defining a real-world problem, how feasible is it to define initial conditions at all? Is there a way to not define initial conditions?

A chaotic system is defined as one that shows sensitivity to initial conditions. That is, any uncertainty in the initial state of the given system, no matter how small, will lead to rapidly growing errors in any effort to predict the future behavior. In other words, the system is chaotic. Its behavior can be predicted only if the initial conditions are known to an infinite degree of accuracy, which is impossible. — Gollub and Solomon