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The Coevolution of Automata in the Repeated Prisoner's Dilemma


  • John H. Miller

    (Carnegie Mellon University, Social and Decision Sciences)


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.

Suggested Citation

  • John H. Miller, "undated". "The Coevolution of Automata in the Repeated Prisoner's Dilemma," Papers _007, Carnegie Mellon, Department of Decision Sciences, revised 22 Mar 1993.
  • Handle: RePEc:wop:carnds:_007

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    Cited by:

    1. Christina Fang & Steven Orla Kimbrough & Stefano Pace & Annapurna Valluri & Zhiqiang Zheng, 2002. "On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt," Group Decision and Negotiation, Springer, vol. 11(6), pages 449-467, November.


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