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Collaboration in networks with randomly chosen agents

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  • Cui, Zhiwei
  • Wang, Rui

Abstract

The present paper considers a finite population of agents located in an arbitrary, fixed network. In each period, a small proportion of agents are randomly chosen to play a minimum effort game. They learn from both their own and their neighbors’ experiences and imitate the most successful choices, though they may occasionally make mistakes. We show that in the long run all agents will choose the highest effort level provided that each agent's neighborhood is large.

Suggested Citation

  • Cui, Zhiwei & Wang, Rui, 2016. "Collaboration in networks with randomly chosen agents," Journal of Economic Behavior & Organization, Elsevier, vol. 129(C), pages 129-141.
    • Handle: RePEc:eee:jeborg:v:129:y:2016:i:c:p:129-141
    DOI: 10.1016/j.jebo.2016.06.015
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    Cited by:

    1. Zhiwei Cui, 2019. "Matching, Imitation, and Coordination in Networks," Dynamic Games and Applications, Springer, vol. 9(1), pages 47-67, March.
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    3. Cabo, Francisco & García-González, Ana, 2020. "Interaction and imitation with heterogeneous agents: A misleading evolutionary equilibrium," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 152-174.
    4. Wang, Zongrun & Chen, Songsheng, 2019. "Market efficiency, strategies and incomes of heterogeneously informed investors in a social network environment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 15-32.
    5. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.

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    More about this item

    Keywords

    Minimum effort game; Rare interaction; Local observation; Imitation;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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