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Carrot or Stick? The Evolution of Reciprocal Preferences in a Haystack Model

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  • Florian Herold

Abstract

We study the evolution of both characteristics of reciprocity: the willingness to reward and the willingness to punish. First, both preferences for rewarding and preferences for punishing can survive provided that individuals interact within separate groups. Second, rewarders survive only in coexistence with self-interested preferences, but punishers either vanish or dominate the population entirely. Third, the evolution of preferences for rewarding and the evolution of preferences for punishing influence each other decisively. Rewarders can invade a population of self-interested players. The existence of rewarders enhances the evolutionary success of punishers, who then crowd out all other preferences. (JEL C71, C72, C73, D64, K42)

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  • Florian Herold, 2012. "Carrot or Stick? The Evolution of Reciprocal Preferences in a Haystack Model," American Economic Review, American Economic Association, vol. 102(2), pages 914-940, April.
    • Handle: RePEc:aea:aecrev:v:102:y:2012:i:2:p:914-40
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    References listed on IDEAS

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    Cited by:

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    2. Adriani, Fabrizio & Sonderegger, Silvia, 2020. "Optimal similarity judgments in intertemporal choice (and beyond)," Journal of Economic Theory, Elsevier, vol. 190(C).
    3. Peña, Jorge & Lehmann, Laurent & Nöldeke, Georg, 2013. "Gains from switching and evolutionary stability in multi-player matrix games," Working papers 2013/13, Faculty of Business and Economics - University of Basel.
    4. Yoshio Kamijo, 2014. "A theory of sanctions: Objectives, degree of heterogeneity, and growth potential matter for optimal use of carrot or stick," Working Papers SDES-2014-13, Kochi University of Technology, School of Economics and Management, revised Oct 2014.
    5. Yuval Heller & Erik Mohlin, 2018. "Observations on Cooperation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2253-2282.
    6. Michaeli, Moti, 2020. "Grouping, in-group bias and the cost of cheating," Games and Economic Behavior, Elsevier, vol. 121(C), pages 90-107.
    7. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    8. Fabrizio Adriani & Silvia Sonderegger, 2018. "The Signaling Value of Punishing Norm-Breakers and Rewarding Norm-Followers," Games, MDPI, vol. 9(4), pages 1-32, December.
    9. Olcina, Gonzalo & Calabuig, Vicente, 2021. "Trust and punishment," European Journal of Political Economy, Elsevier, vol. 70(C).
    10. Alex Barrachina & Víctor González-Chordá, 2016. "To report or not to report: Applying game theory to nursing error reporting," Working Papers 2016/14, Economics Department, Universitat Jaume I, Castellón (Spain).

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    More about this item

    JEL classification:

    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • 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
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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