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Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma

Author

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  • Ashlock, Daniel
  • Smucker, Mark D.
  • Stanley, E. Ann
  • Tesfatsion, Leigh S.

Abstract

This study reports on extensive computer experiments for the "Iterated Prisoner's Dilemma with Choice and Refusal of Partners (IPD/CR)" framework. For example, it is shown that the introduction of choice and refusal tends to enhance the emergence of cooperation in IPD games in comparison to either random or round-robin partner selection. In addition, with choice and refusal of partners, the fitness values of the players tend to cluster fairly tightly into a small number of narrow regions. Within any one fitness region, however, the players consist of genetically diverse players with interaction patterns peculiarly adapted to the choice and refusal mechanism. Detailed sensitivity studies are also reported with respect to key parameters such as the learning algorithm "memory weight," the "minimum tolerance level" below which game offers are refused, and the "refusal payoff" incurred by a player whenever one of his game offers is refused. Annotated pointers to related work can be accessed here: http://www2.econ.iastate.edu/tesfatsi/tnghome.htm

Suggested Citation

  • Ashlock, Daniel & Smucker, Mark D. & Stanley, E. Ann & Tesfatsion, Leigh S., 1996. "Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma," Staff General Research Papers Archive 1687, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:1687
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    References listed on IDEAS

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    1. Hirshlifer, David & Rassmusen, Eric, 1989. "Cooperation in a repeated prisoners' dilemma with ostracism," Journal of Economic Behavior & Organization, Elsevier, vol. 12(1), pages 87-106, August.
    2. Stanley, E.A. & Ashlock, Daniel & Tesfatsion, Leigh, 1994. "Iterated Prisoner's Dilemma with Choice and Refusal of Partners," Staff General Research Papers Archive 11180, Iowa State University, Department of Economics.
    3. Gordon Tullock, 1985. "Adam Smith and the Prisoners' Dilemma," The Quarterly Journal of Economics, Oxford University Press, vol. 100(Supplemen), pages 1073-1081.
    4. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
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    Citations

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    Cited by:

    1. Spiekermann, Kai, 2009. "Sort out your neighbourhood: public good games on dynamic networks," LSE Research Online Documents on Economics 26739, London School of Economics and Political Science, LSE Library.
    2. McFadzean, David & Tesfatsion, Leigh, 1999. "A C++ Platform for the Evolution of Trade Networks," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 109-134, October.
    3. David Hagmann & Troy Tassier, 2014. "Endogenous Movement and Equilibrium Selection in Spatial Coordination Games," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 379-395, October.
    4. Giorgio Fagiolo & Luigi Marengo & Marco Valente, 2004. "Endogenous Networks In Random Population Games," Mathematical Population Studies, Taylor & Francis Journals, vol. 11(2), pages 121-147.
    5. Jack Robles, 2008. "Evolution, bargaining, and time preferences," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 35(1), pages 19-36, April.
    6. Leigh Tesfatsion, 1998. "Teaching Agent-Based Computational Economics to Graduate Students," Computational Economics 9809001, University Library of Munich, Germany, revised 16 Nov 1998.
    7. Hauk, Esther, 1997. "Discriminating to learn to discriminate," UC3M Working papers. Economics 6058, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Leigh S. Tesfatsion, "undated". "An Evolutionary Trade Network Game with Preferential Partner Selection," Computing in Economics and Finance 1996 _057, Society for Computational Economics.
    9. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters,in: Generative Social Science Studies in Agent-Based Computational Modeling Princeton University Press.
    10. Pfeuffer, Wolfgang, 2006. "Religion as a Seed Crystal for Altruistic Cooperation," Munich Dissertations in Economics 5788, University of Munich, Department of Economics.
    11. Esther Hauk, 2003. "Multiple Prisoner's Dilemma Games with(out) an Outside Option: an Experimental Study," Theory and Decision, Springer, vol. 54(3), pages 207-229, May.
    12. Leigh TESFATSION, 1995. "A Trade Network Game With Endogenous Partner Selection," Economic Report 36, Iowa State University Department of Economics.
    13. Moulet, Sonia & Rouchier, Juliette, 2008. "The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2322-2348, July.
    14. Leigh Tesfatsion, 1998. "Gale-Shapley Matching in an Evolutionary Trade Network Game," Game Theory and Information 9805004, University Library of Munich, Germany, revised 26 Jul 1998.
    15. Tesfatsion, Leigh, 1995. "How Economists Can Get Alife," Economic Reports 18196, Iowa State University, Department of Economics.
    16. Esther Hauk, "undated". "Leaving the Prison: A Discussion of the Iterated Prisoner's Dilemma under Preferential Partner Selection," Computing in Economics and Finance 1996 _067, Society for Computational Economics.
    17. Tesfatsion, Leigh, 2001. "Structure, behavior, and market power in an evolutionary labor market with adaptive search," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 419-457, March.
    18. María Pereda & Daniele Vilone, 2017. "Social Pressure and Environmental Effects on Networks: A Path to Cooperation," Games, MDPI, Open Access Journal, vol. 8(1), pages 1-13, January.
    19. M.G. Zimmermann, V. M. Eguiluz, 2001. "Evolution of Cooperative Networks and the Emergence of Leadership," Computing in Economics and Finance 2001 171, Society for Computational Economics.
    20. Robert Axtell, 2007. "What economic agents do: How cognition and interaction lead to emergence and complexity," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 20(2), pages 105-122, September.
    21. 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.
    22. Ladley, Daniel & Wilkinson, Ian & Young, Louise, 2015. "The impact of individual versus group rewards on work group performance and cooperation: A computational social science approach," Journal of Business Research, Elsevier, vol. 68(11), pages 2412-2425.
    23. Seale, Darryl A. & Arend, Richard J. & Phelan, Steven, 2006. "Modeling alliance activity: Opportunity cost effects and manipulations in an iterated prisoner's dilemma with exit option," Organizational Behavior and Human Decision Processes, Elsevier, vol. 100(1), pages 60-75, May.
    24. Nicolaas J. Vriend, 2005. "ACE Models of Endogenous Interactions," Working Papers 542, Queen Mary University of London, School of Economics and Finance.
    25. Janssen, Marco A., 2008. "Evolution of cooperation in a one-shot Prisoner's Dilemma based on recognition of trustworthy and untrustworthy agents," Journal of Economic Behavior & Organization, Elsevier, vol. 65(3-4), pages 458-471, March.

    More about this item

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

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