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

Author

Listed:
  • Dan Ashlock

    (Dept. of Mathematics, Iowa State Univ.)

  • Mark D. Smucker

    (Dept. of Computer Sciences, U of Wisc.-Madison)

  • E. Ann Stanley

    (Dept. of Math, Iowa State U)

  • Leigh Tesfatsion

    (Dept. of Econ and Dept. of Math, Iowa State U)

Abstract

Partner selection is an important process in many social interactions, permitting individuals to decrease the risks associated with cooperation. In large populations, defectors may escape punishment by roving from partner to partner, but defectors in smaller populations risk social isolation. We investigate these possibilities for an evolutionary prisoner's dilemma in which agents use expected payoffs to choose and refuse partners. In comparison to random or round-robin partner matching, we find that the average payoffs attained with preferential partner selection tend to be more narrowly confined to a few isolated payoff regions. Most ecologies evolve to essentially full cooperative behavior, but when agents are intolerant of defections, or when the costs of refusal and social isolation are small, we also see the emergence of wallflower ecologies in which all agents are socially isolated. In between these two extremes, we see the emergence of ecologies whose agents tend to engage in a small number of defections followed by cooperation thereafter. The latter ecologies exhibit a plethora of interesting social interaction patterns. Keywords: Evolutionary Game; Iterated Prisoner's Dilemma; Partner Choice and Refusal; Artificial Life; Genetic Algorithm; Finite Automata

Suggested Citation

  • Dan Ashlock & Mark D. Smucker & E. Ann Stanley & Leigh Tesfatsion, 1995. "Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma," Game Theory and Information 9501002, EconWPA, revised 20 Jan 1995.
  • Handle: RePEc:wpa:wuwpga:9501002
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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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    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. Hauk, Esther, 1997. "Discriminating to learn to discriminate," UC3M Working papers. Economics 6058, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Leigh S. Tesfatsion, "undated". "An Evolutionary Trade Network Game with Preferential Partner Selection," Computing in Economics and Finance 1996 _057, Society for Computational Economics.
    8. 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.
    9. Pfeuffer, Wolfgang, 2006. "Religion as a Seed Crystal for Altruistic Cooperation," Munich Dissertations in Economics 5788, University of Munich, Department of Economics.
    10. 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.
    11. Tesfatsion, Leigh S., 1998. "Teaching Agent-Based Computational Economics to Graduate Students," Staff General Research Papers Archive 1199, Iowa State University, Department of Economics.
    12. 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.
    13. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics.
    14. Leigh TESFATSION, 1995. "A Trade Network Game With Endogenous Partner Selection," Economic Report 36, Iowa State University Department of Economics.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Tesfatsion, Leigh S., 1998. "Gale-Shapley Matching in an Evolutionary Trade Network Game," Staff General Research Papers Archive 1230, Iowa State University, Department of Economics.
    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. 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:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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