社会困境中从众与基于报酬的模仿之间的竞争与伙伴关系
Competition and partnership between conformity and payoff-based imitations in social dilemmas
摘要：
向获得更高回报的伙伴学习是进化博弈论中经常使用的工作假设。另一种动态规则是焦点参与者倾向于遵循局部邻域中大多数参与者的策略选择，这通常被称为一致性驱动的策略更新。在这项工作中，我们假设这两种策略学习方法是存在的，并在一个共同进化模型的框架内竞争空间。我们的研究结果表明，存在的回报驱动的策略学习方法成为独家高成功者的回报和/或高诱惑值，代表了雪堆游戏困境的情况。然而，在一般情况下，上述策略学习方法的竞争可能是有用的，以扩大参数空间，只有合作者占上风。这种合作的成功是基于合作者参与者的强制协调，这揭示了后一种策略的好处。有趣的是，基于收益的合作者和基于一致性的合作者可以形成一个有效的联盟来对抗背叛者，这也可以扩展猎鹿博弈区域中完全合作者解的参数空间。我们的工作强调了策略和个体特征如学习方法的协同进化可以提供一种新的斑图形成机制，这种机制在静态模型中无法观察到，因此仍然隐藏在传统模型中。
英文摘要：
Learning from a partner who collects higher payoff is a frequently used working hypothesis in evolutionary game theory. One of the alternative dynamical rules is when the focal player prefers to follow the strategy choice of the majority in the local neighborhood, which is often called as conformity-driven strategy update. In this work we assume that both strategy learning methods are present and compete for space within the framework of a coevolutionary model. Our results reveal that the presence of payoff-driven strategy learning method becomes exclusive for high succer's payoff and/or high temptation values that represent a snowdrift game dilemma situation. In general, however, the competition of the mentioned strategy learning methods could be useful to enlarge the parameter space where only cooperators prevail. This success of cooperation is based on the enforced coordination of cooperator players which reveals the benefit of the latter strategy. Interestingly, the payoff-based and the conformity-based cooperator players can form an effective alliance against defectors that can also extend the parameter space of full cooperator solution in the stag-hunt game region. Our work highlights that the coevolution of strategies and individual features such as learning method can provide novel type of pattern formation mechanism that cannot be observed in a static model, hence remains hidden in traditional models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
