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The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

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  • van Bragt, David
  • van Kemenade, Cees
  • la Poutre, Han

Abstract

Many economic and social systems are essentially large multi-agent systems. By means of computational modeling, the complicated behavior of such systems can be investigated. Modeling a multi-agent system as an evolutionary agent system, several important choices have to be made for evolutionary operators. Especially, it is to be expected that evolutionary dynamics substantially depend on the selection scheme. We therefore investigate the influence of evolutionary selection mechanisms on a fundamental problem: the iterated prisoner's dilemma (IPD), an elegant model for the emergence of cooperation in a multi-agent system. We observe various types of behavior, cooperation level, and stability, depending on the selection mechanism and the selection intensity. Hence, our results are important for (1) the proper choice and application of selection schemes when modeling real economic situations and(2) assessing the validity of the conclusions drawn from computer experiments with these models. We also conclude that the role of selection in the evolution of multi-agent systems should be investigated further, for instance using more detailed and complex agent interaction models. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • van Bragt, David & van Kemenade, Cees & la Poutre, Han, 2001. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 253-263, June.
    • Handle: RePEc:kap:compec:v:17:y:2001:i:2-3:p:253-63
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    References listed on IDEAS

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    1. Ken Binmore & Nir Vulkan, 1999. "Applying game theory to automated negotiation," Netnomics, Springer, vol. 1(1), pages 1-9, October.
    2. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
    3. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    4. Ken Binmore & Nir Vulkan, "undated". "Applying Game Theory to Automated Negotiation," ELSE working papers 004, ESRC Centre on Economics Learning and Social Evolution.
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    1. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    2. D.D.B. Bragt, van & J. A. La Poutr & E. H. Gerding, 2000. "Equilibrium Selection In Evolutionary Bargaining Models," Computing in Economics and Finance 2000 323, Society for Computational Economics.
    3. D.D.B. van Bragt & J.A. La Poutré, 2003. "Why Agents for Automated Negotiations Should Be Adaptive," Netnomics, Springer, vol. 5(2), pages 101-118, November.
    4. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    6. Alan G. Isaac, 2008. "Simulating Evolutionary Games: A Python-Based Introduction," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-8.

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