为研究用于划分分组的参数、个体的记忆长度、网络的聚类性等参数对合作行为的共同影响,在传统的复杂网络模型结合博弈论模型的研究基础上,提出一种分组选择的个体互动规则。通过仿真实验发现,分组选择的方法不仅可以有效促进可调聚类网络中合作行为的涌现,同样也可以促进方格网和小世界网络中个体的合作,该规则具有一定的普适性和可重复性。该研究提供了一种提高大规模群体的整体合作水平的方法,能够更深入地了解现实世界的合作现象,为探索个体合作行为背后的机理提供了一个新途径。
Cooperative behavior is widespread, and how to promote the emergence of cooperative behavior has been a hot issue of systems science. Combining the traditional complex network model with the game theory model, this paper proposes a grouping selection rule for individual interaction and investigates the joint influence of the parameter dividing the groups, the individual's memory length, and the network clustering coefficient on cooperative behavior. Through simulation experiments, it is found that the grouping selection can effectively promote not only the emergence of cooperative behaviors in adjustable clustering networks, but also the individuals' cooperation in lattice and small-world networks, indicating that the rule has certain universality and repeatability. It provides a new approach to improve the overall level of cooperation in large-scale groups, enabling us to gain a deeper understanding of real-world cooperation phenomena and opening up a new avenue for exploring the reasons behind individuals' cooperative behavior.
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