当群体具有吸引力：合作与个体及群体模仿规则的共同进化动力学

中文摘要 AI 辅助

成功驱动的模仿是社会学习的一个基本方面，然而超越成对交互的群体互动需要更微妙的模仿规则，因为成功可以在群体或个体层面上被观察到。基于这些不同社会信息层面的启发式模仿规则如何与集体行为（尤其是合作）竞争并共同进化，仍然知之甚少。在此，我们通过研究公共品博弈中合作与超图上基于个体和群体的模仿规则的共同进化动力学来解决这一问题。我们根据收益偏好的模仿是在个体层面、群体层面还是两者兼有，区分了三种规则。我们的结果揭示了合作与群体偏向模仿之间的相互强化：优先向成功群体学习促进合作，而合作者反过来又 favoring 群体偏向模仿。我们在多样化的合成和经验高阶网络群体中确认了合作与群体偏向模仿之间的这种协同作用。我们进一步表明，群体交互的微妙尺度和组织关键地塑造了这种共同进化，其中中等群体规模为合作提供了最大优势。我们的结果揭示了学习模仿谁可以共同进化以塑造集体合作。

英文摘要

Success-driven imitation is a fundamental aspect of social learning, yet group interactions beyond pairwise require more subtle imitating rules since success can be observed at either the group or individual level. How such heuristic imitating rules based on these distinct levels of social information compete and coevolve with collective behavior, particularly cooperation, remains poorly understood. Here, we address this issue by studying the coevolutionary dynamics of cooperation in public goods games and individual- and group-based imitating rules on hypergraphs. We distinguish three rules according to whether payoff-biased imitation operates at the individual level, the group level, or both. Our results identify a mutual reinforcement between cooperation and group-biased imitation: preferentially learning from successful groups promotes cooperation, while cooperators in turn favor group-biased imitation. We confirm this synergy between cooperation and group-biased imitation across diverse synthetic and empirical higher-order network populations. We further show that the subtle scale and organization of group interactions critically shape this coevolution, with intermediate group sizes providing the greatest advantage for cooperation. Our results reveal how learning whom to imitate can jointly evolve to shape collective cooperation.

发表机构

• Tongji University（同济大学）
• Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自动驾驶系统研究院）
• Dartmouth College（达特茅斯学院）
• Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）

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