Prediction and Adaptation in an Evolving Chaotic Environment

We describe the results of analytic calculations and computer simulations of adaptive predictors (predictive agents) responding to an evolving chaotic environment and to one another. Our simulations are designed to quantify adaptation and to explore co-adaptation for a simple calculable model of a complex adaptive system. We first consider the ability of a single agent, exposed to a chaotic environment, to model, control, and predict the future states of that environment. We then introduce a second agent which, in attempting to model and control both the chaotic environment and the first agent, modifies the extent to which that agent can identify patterns and exercise control. We find that (i) optimal adaptive predictors have an optimal memory and an optimal complexity, which are small for a a rapidly changing map dynamics and (ii) that the predictive power can be increased by imposing chaos or random noise onto the map dynamics. The competition between the two predictive agents can lead either to chaos, or to metastable emergent behavior, best described as a leader-follower relationship. Our results suggest a correlation between optimal adaptation, optimal complexity, and emergent behavior, and provide preliminary support for the concept of optimal co-adaptation near the edge of chaos.