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Evolutionary Games and Matching Rules

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Abstract

This study considers evolutionary games with non-uniformly random matching when interaction occurs in groups of n >= 2 individuals using pure strategies from a finite strategy set. In such models, groups with different compositions of individuals generally co-exist and the reproductive success (fitness) of a specific strategy varies with the frequencies of different group types. These frequencies crucially depend on the matching process. For arbitrary matching processes (called matching rules), we study Nash equilibrium and ESS in the associated population game and show that several results that are known to hold for population games under uniform random matching carry through to our setting. In our most novel contribution, we derive results on the efficiency of the Nash equilibria of population games and show that for any (fixed) payoff structure, there always exists some matching rule leading to average fitness maximization. Finally, we provide a series of applications to commonly studied normal-form games.

Suggested Citation

  • Jensen, Martin Kaae & Rigos, Alexandros, 2017. "Evolutionary Games and Matching Rules," Working Papers 2017:11, Lund University, Department of Economics, revised 06 Mar 2018.
  • Handle: RePEc:hhs:lunewp:2017_011
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    Cited by:

    1. Alger, Ingela, 2020. "Monogamy: exception or rule?," TSE Working Papers 20-1093, Toulouse School of Economics (TSE).
    2. Tobias Hiller, 2018. "On the Stability of Couples," Games, MDPI, Open Access Journal, vol. 9(3), pages 1-1, July.
    3. Jiabin Wu, 2020. "Labelling, homophily and preference evolution," International Journal of Game Theory, Springer;Game Theory Society, vol. 49(1), pages 1-22, March.
    4. Jiabin Wu, 2019. "Social connections and cultural heterogeneity," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 779-798, April.
    5. Alger, Ingela & Weibull, Jörgen W., 2018. "Evolutionary Models of Preference Formation," IAST Working Papers 18-82, Institute for Advanced Study in Toulouse (IAST).
    6. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, Open Access Journal, vol. 9(2), pages 1-1, May.

    More about this item

    Keywords

    evolutionary game theory; evolutionarily stable strategy; ESS; non-uniformly random matching;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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