Abstract
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.
Recommended Citation
Krovi, Ravi, "An Artificial Laboratory Environment for Studying Distributed Decision
Processes." (1996). AMCIS 1996 Proceedings. 62.
https://aisel.aisnet.org/amcis1996/62