COLLECTIVE BEHAVIOR OF PREDICTIVE AGENTS

We investigate the effect of predictions upon a model of coevolutionary systems which was originally inspired by computational ecosystems. The model incorporates many of the features of distributed resource allocation in systems comprised of many individual agents, including asynchrony, resource contention, and decision-making based upon incomplete knowledge and delayed information. Previous analyses of a similar model of non-predictive agents have demonstrated that periodic or chaotic oscillations in resource allocation can occur under certain conditions, and that these oscillations can affect the performance of the system adversely. In this work, we show that the system performance can be improved if the agents do an adequate job of predicting the current state of the system. We explore two plausible methods for prediction - technical analysis and system analysis. Technical analysts are responsive to the behavior of the system, but suffer from an inability to take their own behavior into account. System analysts perform extremely well when they have very accurate information about the other agents in the system, but can perform very poorly when their information is even slightly inaccurate. By combining the strengths of both methods, we obtain a successful hybrid of the two prediction methods which adapts its model of other agents in response to the observed behavior of the system. © 1990.