We examine the feasibility of using an adaptive systems approach for generating the non-linear and dynamic aspects of distributed decision processes. First, the issues that need to be considered in modeling agent interactions are discussed. We then present details of a computational prototype based on the interplay of agents and their actions. Our model represents agent decision making as an adaptive search activity. The agents in our model learn by using a system that rewards strategies that generate high payoffs and penalize strategies that do not.
Krovi, Ravi, "An Artificial Laboratory Environment for Studying Distributed Decision
Processes." (1996). AMCIS 1996 Proceedings. 63.