Zhang Gui, Yao Yichao, Zeng Ziyan, Feng Minyu, Chica Manuel
The College of Artificial Intelligence, Southwest University, No. 2 Tiansheng Road, Beibei, Chongqing 400715, China.
Department of Computer Science and A.I. Andalusian Research Institute DaSCI "Data Science and Computational Intelligence," University of Granada, 18071 Granada, Spain.
Chaos. 2025 Jan 1;35(1). doi: 10.1063/5.0250120.
Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise interaction rules and the punishment mechanism overlook this aspect. Building on this observation, this paper enhances a spatial public goods game in two key ways: (1) We set a reputation threshold and use punishment to regulate the defection behavior of players in low-reputation groups while allowing defection behavior in high-reputation game groups. (2) Differently from pairwise interaction rules, we combine reputation and payoff as the fitness of individuals to ensure that players with both high payoff and reputation have a higher chance of being imitated. Through simulations, we find that a higher reputation threshold, combined with a stringent punishment environment, can substantially enhance the level of cooperation within the population. This mechanism provides deeper insight into the widespread phenomenon of cooperation that emerges among individuals.
声誉和惩罚是规范人类社会中个体行为的重要准则,声誉良好的人更有可能被他人模仿。此外,社会对损害不同声誉群体利益的行为施加不同程度的惩罚。然而,传统的两两互动规则和惩罚机制忽略了这一方面。基于这一观察,本文从两个关键方面改进了一个空间公共品博弈:(1)我们设定一个声誉阈值,利用惩罚来规范低声誉群体中参与者的背叛行为,同时允许高声誉博弈群体中的背叛行为。(2)与两两互动规则不同,我们将声誉和收益结合起来作为个体的适应度,以确保收益和声誉都高的参与者有更高的被模仿机会。通过模拟,我们发现较高的声誉阈值与严格的惩罚环境相结合,可以大幅提高群体内部的合作水平。这种机制为个体间出现的广泛合作现象提供了更深入的见解。