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Group size and cooperation among strangers

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  • Duffy, John
  • Xie, Huan

Abstract

We study how group size affects cooperation in an infinitely repeated n-player Prisoner's Dilemma (PD) game. In each repetition of the game, groups of size n≤M are randomly and anonymously matched from a fixed population of size M to play the n-player PD stage game. We provide conditions for which the contagious strategy (Kandori, 1992) sustains a social norm of cooperation among all M players. Our main finding is that if agents are sufficiently patient, a social norm of society-wide cooperation becomes easier to sustain under the contagious strategy as n increases toward M. In an experiment where the population size M is fixed and conditions identified by our theoretical analysis hold, we find strong evidence that cooperation rates are higher with larger group sizes than with smaller group sizes in treatments where each subject interacts with M−1 robot players who follow the contagious strategy. When the number of human subjects increases in the population, the cooperation rates decrease significantly, indicating that it is the strategic uncertainty among the human subjects that hinders cooperation.

Suggested Citation

  • Duffy, John & Xie, Huan, 2016. "Group size and cooperation among strangers," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 55-74.
  • Handle: RePEc:eee:jeborg:v:126:y:2016:i:pa:p:55-74
    DOI: 10.1016/j.jebo.2016.02.007
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    2. Ghidoni, Riccardo & Cleave, Blair L. & Suetens, Sigrid, 2019. "Perfect and imperfect strangers in social dilemmas," European Economic Review, Elsevier, vol. 116(C), pages 148-159.
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    4. Jens Gudmundsson & Jens Leth Hougaard, 2020. "Enabling reciprocity through blockchain design," IFRO Working Paper 2020/14, University of Copenhagen, Department of Food and Resource Economics, revised 09 Feb 2021.
    5. Iván Barreda-Tarrazona & Ainhoa Jaramillo-Gutiérrez & Marina Pavan & Gerardo Sabater-Grande, 2021. "The “Human Factor” in Prisoner’s Dilemma Cooperation," Working Papers 2021/10, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. Christoph March, 2019. "The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players," CESifo Working Paper Series 7926, CESifo.
    7. Normann, Hans-Theo & Sternberg, Martin, 2022. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," DICE Discussion Papers 392, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    8. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    9. Helénsdotter, Ronja, 2019. "Experimental Evidence on Cooperation, Political Affiliation, and Group Size," Working Papers in Economics 765, University of Gothenburg, Department of Economics.
    10. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
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    12. Serdarevic, Nina & Strømland, Eirik & Tjøtta, Sigve, 2018. "It Pays to be Nice: The Benefits of Cooperating in Markets," Working Papers in Economics 12/18, University of Bergen, Department of Economics.

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    More about this item

    Keywords

    Cooperation; Social norms; Group size; Repeated games; Random matching; Prisoner's Dilemma; Experiment;
    All these keywords.

    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
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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