发表机构
College of Science, Northwest A&F University; College of Economics & Management, Northwest A&F University（西北农林科技大学理学院; 西北农林科技大学经济管理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建随机顺序引导模型，引入引导者随机排序、终止条件及个体易说服/不易说服心理特质，发现其能有效促进合作并缓解二阶搭便车问题，且合作在更强说服力、更低成本、更大群体下更繁荣。
AI 中文摘要
社会引导作为一种促进集体合作的普遍机制，通过纠正背叛者的行为，有效维持并提升群体的合作水平。然而，现有引导模型存在两个关键局限：其一，它们假设引导行为必然成功；其二，它们忽视了引导者之间的信息共享，导致对同一已成功引导的背叛者进行重复引导。此外，现有模型往往未充分考虑个体的心理特征。在此，我们通过引入引导者的随机排序和引导行为的终止条件，构建了一个随机顺序引导模型，以优化引导效率。同时，我们将个体心理特质，如易说服（说服成功率随尝试次数增加而提高）和不易说服（说服成功率随尝试次数增加而降低），纳入建模框架。我们发现，随机顺序引导的引入不仅能有效促进合作，还能缓解二阶搭便车问题。重要的是，合作在更强的说服力、更低的成本和更大的群体规模下得以蓬勃发展。
英文摘要
Social guidance, as a universal mechanism to promote collective cooperation, effectively maintains and enhances the level of population cooperation by correcting the behavior of defectors. However, existing guidance models have two key limitations: first, they assume that guidance actions are inevitably successful; second, they neglect information sharing among guides, leading to repeated guidance of the same successfully guided defector. Additionally, the psychological characteristics of individuals are often not fully considered in existing models. Here, we construct a stochastic sequence guidance model by introducing random ordering of guides and termination conditions for guidance actions to optimize the efficiency of guidance. Besides, we incorporate individual psychological traits such as persuasion-prone (where the success rate of persuasion increases with attempts) and persuasion-averse (where the success rate of persuasion decreases with attempts) into the modeling framework. We find that the introduction of random sequence guidance can not only effectively promote cooperation but also mitigate the second-order free-rider problem. Importantly, cooperation thrives with stronger persuasion, lower costs, and larger groups.
