Cooperation among unequal players with aspiration-driven learning

互惠(文化人类学) 随机博弈 不平等 生产力 任务(项目管理) 微观经济学 经济 计算机科学 社会心理学 心理学 数学 数学分析 管理 宏观经济学
作者
Fang Chen,Lei Zhou,Long Wang
出处
期刊:Journal of the Royal Society Interface [The Royal Society]
卷期号:21 (212) 被引量:1
标识
DOI:10.1098/rsif.2023.0723
摘要

Direct reciprocity promotes the evolution of cooperation when players are sufficiently equal, such that they have similar influence on each other. In the light of ubiquitous inequality, this raises the question of how reciprocity evolves among unequal players. Existing studies on inequality mainly focus on payoff-driven learning rules, which rely on the knowledge of others' strategies. However, inferring one's strategy is a difficult task even if the whole interaction history is known. Here, we consider aspiration-driven learning rules, where players seek strategies that satisfy their aspirations based on their own information. Under aspiration-driven learning rules, we explore the evolutionary dynamics among players with inequality in endowments and productivity. We model the interactions among unequal players with asymmetric games and characterize the condition where cooperation is feasible. Remarkably, we find that aspiration-driven learning rules lead to a higher level of cooperation than payoff-driven ones over a wide range of inequality. Moreover, our results show that high aspiration levels are conducive to the evolution of cooperation when more productive players are equipped with higher endowments. Our work highlights the advantages of aspiration-driven learning for promoting cooperation among unequal players and suggests that aspiration-based decision-making may be more beneficial for the collective.
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