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The Optimal Incentive In Promoting Cooperation: Punish The Worst And Do Not Only Reward The Best

Author

Listed:
  • SHUTING HUANG

    (Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, P. R. China)

  • YALI DONG

    (��School of Systems Science, Beijing Normal University, Xinjiekouwai Street No. 19, Beijing 100875, P. R. China)

  • BOYU ZHANG

    (Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, P. R. China)

Abstract

Incentive institutions that reward cooperators and punish free-riders are often used to promote cooperation in public goods games. We show that for incentives of intermediate size, a sanctioning institution that punishes the worst players can sustain full cooperation and that a rewarding institution can promote cooperation only if lower contributors also have the chance to win the reward. Furthermore, if the incentive institution can provide both reward and punishment, then it should use reward as much as possible. The group welfare is maximized when the punishment is just barely larger than the minimum required to obtain the full contribution.

Suggested Citation

  • Shuting Huang & Yali Dong & Boyu Zhang, 2024. "The Optimal Incentive In Promoting Cooperation: Punish The Worst And Do Not Only Reward The Best," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 69(07), pages 2281-2293, December.
    • Handle: RePEc:wsi:serxxx:v:69:y:2024:i:07:n:s0217590820500071
    DOI: 10.1142/S0217590820500071
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    More about this item

    Keywords

    Public goods game; cooperation; reward; punishment;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact

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