The Coevolution of Automata in the Repeated Prisoner's Dilemma

A model of learning and adaptation is used to analyze the coevolution of strategies in the repeated Prisoner's Dilemma game under both perfect and imperfect reporting. Meta-players submit finite automata strategies and update their choices through an explicit evolutionary process modeled by a genetic algorithm. Using this framework, adaptive strategic choice and the emergence of cooperation are studied through ``computational experiments.'' The results of the analyses indicate that information conditions lead to significant differences among the evolving strategies. Furthermore, they suggest that the general methodology may have much wider applicability to the analysis of adaptation in economic and social systems.Adaptation, Evolution, Learning, Repeated Prisoner's Dilemma game, Genetic Algorithms, Machine Learning.