The emergence of cooperative behavior, despite natural selection favoring rational self-interest, presents a significant evolutionary puzzle. Evolutionary game theory elucidates why cooperative behavior can be advantageous for survival. However, the impact of non-uniformity in the frequency of actions, particularly when actions are altered in the short term, has received little scholarly attention. To demonstrate the relationship between the non-uniformity in the frequency of actions and the evolution of cooperation, we conducted multi-agent simulations of evolutionary games. In our model, each agent performs actions in a chain-reaction, resulting in a non-uniform distribution of the number of actions. To achieve a variety of non-uniform action frequency, we introduced two types of chain-reaction rules: one where an agent's actions trigger subsequent actions, and another where an agent's actions depend on the actions of others. Our results revealed that cooperation evolves more effectively in scenarios with even slight non-uniformity in action frequency compared to completely uniform cases. In addition, scenarios where agents' actions are primarily triggered by their own previous actions more effectively support cooperation, whereas those triggered by others' actions are less effective. This implies that a few highly active individuals contribute positively to cooperation, while the tendency to follow others' actions can hinder it.

翻译：尽管自然选择倾向于理性的自利行为，合作行为的涌现仍构成了一个重要的演化谜题。演化博弈理论阐明了合作行为为何可能有利于生存。然而，行动频率的非均匀性——特别是当行动在短期内发生改变时——的影响很少受到学术关注。为揭示行动频率的非均匀性与合作演化之间的关系，我们进行了演化博弈的多智能体模拟。在我们的模型中，每个智能体的行动以链式反应方式执行，导致行动次数呈非均匀分布。为实现多样化的非均匀行动频率，我们引入了两种链式反应规则：一种是智能体的行动触发后续行动，另一种是智能体的行动依赖于其他智能体的行动。我们的结果表明，与完全均匀的情况相比，即使在行动频率存在轻微非均匀性的场景中，合作也能更有效地演化。此外，当智能体的行动主要由其自身先前的行动触发时，更有利于合作；而由他人行动触发的场景则效果较差。这意味着少数高活跃度个体对合作有积极贡献，而跟随他人行动的倾向可能阻碍合作。