Applying genetic algorithms to economy market using iterated prisoner's dilemma

• Authors: Raymond Chiong
• Venue: Proceedings of the 2007 ACM symposium on Applied computing
• Year: 2007

This paper investigates the use of Genetic Algorithms (GA) to evolve cooperative agents in a competitive market environment using Iterated Prisoner's Dilemma (IPD). Our study seeks to follow Axelrod's research of computer simulations of the IPD game which is generally regarded as a benchmark for the studies on evolution of cooperation. However, we are of the opinion that his work was a little restrictive and lack of a genuine real-world component. In this paper, we report on a simulation study that attempts to bridge the gap by applying GA to a market model. We examine how well GA could perform against the IPD strategies, and explore the strategic interactions among the agents that represent firms in a coevolving population. We also report on the influence of the genetic operators on the performance of GA. Our experimental results show that cooperation can be evolved in such non-cooperative environment using GA. We conclude that with proper tuning of parameters, GA could be extremely useful for optimizing the outcome of an economy market.