Wang Dini, Yi Peng, Hong Yiguang, Chen Jie, Yan Gang
College of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
Shanghai Research Institute for Intelligent Autonomous Systems, State Key Laboratory of Autonomous Intelligent Unmanned Systems, MOE Frontiers Science Center for Intelligent Autonomous Systems, and Shanghai Key Laboratory of Intelligent Autonomous Systems, Tongji University, Shanghai, People's Republic of China.
PLoS Comput Biol. 2025 Aug 4;21(8):e1012891. doi: 10.1371/journal.pcbi.1012891. eCollection 2025 Aug.
Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the group-mutual comparison update - selecting a high-fitness group and then imitating a random individual within this group - can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems.
合作是人类社会的基础,个体之间的互动结构深刻地塑造了合作的产生和演变。在现实世界的场景中,合作不仅存在于二元行为中,在多群体(高阶)人群中也普遍存在。尽管最近对高阶网络中的群体困境进行了研究,但由于群体层面互动的复杂性和不可分割性,由高阶策略更新驱动的合作探索仍然有限。在这里,我们研究了公共物品博弈中策略更新的四类高阶机制,并建立了合作出现的数学条件。这些条件揭示了高阶策略更新和网络属性对进化结果的影响,特别强调了群体间重叠对合作的促进作用。有趣的是,我们发现群体相互比较更新——选择一个高适应性群体,然后模仿该群体中的一个随机个体——可以显著促进合作。我们的分析进一步揭示,与两两互动相比,高阶策略更新通常在大多数高阶网络中提高合作水平。这些发现强调了高阶策略更新在促进复杂社会系统中的集体合作方面的关键作用。