Prediction and adaptation in an evolving chaotic environment

We describe work in progress on computer simulations of adaptive predictive agents responding to an evolving chaotic environment and to one another. Our simulations are designed to quantify adaptation and to expore 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. 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 adapatation, optimal complexity, and emergent behavior, and provide preliminary support for the concept of optimal co-adaptation near the edge of chaos.

[1]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[2]  Leon O. Chua,et al.  Synchronization of chaos in phase-locked loops , 1991 .

[3]  M. Feigenbaum.  Quantitative universality for a class of nonlinear transformations , 1978 .

[4]  S. Kauffman,et al.  Adaptive automata based on Darwinian selection , 1986 .

[5]  John E. Hauser.  Modeling and Control of Nonlinear Systems , 1994 .

[6]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[7]  Kunihiko Kaneko,et al.  Evolution to the Edge of Chaos in Imitation Game , 1993 .

[8]  E. A. Jackson,et al.  Perspectives of nonlinear dynamics , 1990 .

[9]  E. A. Jackson,et al.  Entrainment and migration controls of two-dimensional maps , 1992 .

[10]  Young,et al.  Inferring statistical complexity. , 1989, Physical review letters.

[11]  General resonance spectroscopy , 1991 .

[12]  Jackson Ea,et al.  Controls of dynamic flows with attractors. , 1991 .

[13]  S. Wolfram.  Statistical mechanics of cellular automata , 1983 .

[14]  W. Arthur.  Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality , 1991 .

[15]  Jay E. Mittenthal.  Principles Of Organization In Organisms , 1992 .

[16]  L. Keefe,et al.  Two nonlinear control schemes contrasted on a hydrodynamiclike model , 1993 .

[17]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[18]  Lüscher,et al.  Scaling behavior of the maximum energy exchange between coupled anharmonic oscillators. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[19]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[20]  Ingo Rechenberg,et al.  The Evolution Strategy. A Mathematical Model of Darwinian Evolution , 1984 .

[21]  Dinkelacker,et al.  Noise in the modeling and control of dynamical systems. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[22]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[23]  Jackson.  Controls of dynamic flows with attractors. , 1991, Physical Review A. Atomic, Molecular, and Optical Physics.

[24]  H. Poincaré,et al.  Les méthodes nouvelles de la mécanique céleste , 1899 .

[25]  H. Haken,et al.  The influence of noise on the logistic model , 1981 .