Strategy update rules play an important role in repeated Prisoner's Dilemma games. This work proposes a modified strategy update rule based on the traditional Fermi function, in which individual past performance is taken into account in strategy update. Then, the consistency aspiration α serves as a benchmark to measure an individual's past performance, and the past performance score is dynamically adjusted during the evolution process according to the BM reinforcement learning rules. The computational results indicate that the proposed modified strategy update rules can significantly improve system cooperation t than the traditional version, and the network reciprocity effect is enhanced as the result of past performance is coupled into the strategy update rule. Moreover, different temptation to defection b exist a corresponding aspiration α result in maximizing system cooperation. Furthermore, an optimal sensitivity level β can also result in a maximizing system cooperation. As a whole, for α − β phase diagram, different a will correspond to an optimal value β that allows the system to achieve the maximum cooperation. Finally, the proposed mechanism is robust. Hopefully this can help to inspire further research on how to deal with social dilemmas.

中文翻译：

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过去绩效驱动的策略更新促进空间囚徒困境博弈中的合作


策略更新规则在重复的囚徒困境游戏中发挥着重要作用。这项工作提出了一种基于传统费米函数的改进策略更新规则，其中在策略更新中考虑了单个过去的表现。然后，一致性期望α作为衡量个体过去表现的基准，在进化过程中根据BM强化学习规则动态调整过去的表现分数。计算结果表明，与传统版本相比，所提出的修改策略更新规则可以显著提高系统协作能力，并且将过去性能的结果耦合到策略更新规则中，网络互易效应增强。此外，不同的背叛诱惑 b 存在相应的愿望α导致系统合作最大化。此外，β最佳灵敏度水平还可以最大限度地提高系统协作。总的来说，对于 α − β 相图，不同的 a 将对应于允许系统实现最大合作的最佳值 β。最后，所提出的机制是稳健的。希望这可以帮助激发关于如何处理社会困境的进一步研究。