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Reward and Punishment Mechanism with weighting enhances cooperation in evolutionary games

Author

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  • Zu, Jinjing
  • Xu, Fanxin
  • Jin, Tao
  • Xiang, Wei

Abstract

The essential of the human cooperative behavior is to find an effective mechanism to promote cooperation. In reality, there is a class of professionals who punish evil and promote good, such as police officers, judges, and volunteers. Their actions and responsibilities can profoundly affect the actions of other individuals by fines and bonuses. Motivated by this phenomenon, we propose a Reward and Punishment Mechanism with weighting (RPMW) for promoting cooperation. The core of RPMW lies in three aspects: (i) A subset of players are tagged as supervisors, who are generated by a random algorithm. If the supervisor chooses to cooperate, she/he acts as the doer of rewards and punishments, otherwise she/he is a general player. (ii) Each supervisor asks their neighbors in turn as well as rewards cooperators and punishes defectors. Bonuses (penalties) are calculated according to a weighting which is the ratio of the number of current evolutionary round cooperators (defectors) to the total of players. (iii) A factor α is added to control the intensity of rewards and punishments, and the β is denoted as the percentage of supervisors in the group. Furthermore, we do a detailed ablation study on the Reward Mechanism with Weighting (RMW) and Punishment Mechanism with Weighting (PMW) in three social dilemmas. The simulation results show that RPMW can effectively promote cooperation in prisoner’s dilemma (PDG), snowdrift game (SDG) and stag-hunt game (SHG). We find that the frequency of cooperation reaches highest when the α is at its maximum and the β is in a positive impact on evolutionary cooperation. The results prove that RPMW is more effective than RMW and PMW on enhancing cooperation in PDG, SDG.

Suggested Citation

  • Zu, Jinjing & Xu, Fanxin & Jin, Tao & Xiang, Wei, 2022. "Reward and Punishment Mechanism with weighting enhances cooperation in evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007233
    DOI: 10.1016/j.physa.2022.128165
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