个体在社会交往中的有限理性如何影响合作的进化动力

Somayeh Koohborfardhaghighi, J. P. Romero, Sira Maliphol, Yulin Liu, J. Altmann
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引用次数: 1

摘要

在本研究中,我们探讨了囚徒困境进化博弈中合作行为的出现。特别地,我们研究了个体的有限理性对网络拓扑(即个体的个人网络)的影响。为此,我们强调了在不同图拓扑的基础属性(如平均最短路径长度和聚类系数)上合作的进化动力学。此外,我们测试了一个新的变量,称为交互记忆,对行为和决策的变化以及网络结果的影响。为此,我们使用基于代理的建模,它允许研究环境的变化或网络参与者属性的变化如何影响他们之间合作的进化动态。我们的分析结果证实了网络拓扑结构和记忆持续时间对参与者合作行为的产生有重要影响。它们还会影响从囚徒困境进化博弈中获得的总效用。尽管囚徒困境游戏模拟倾向于完全合作,但如果它们运行在具有较短平均最短路径长度和较低聚类系数的图拓扑上,则达到平衡所需的步骤数量会增加。这一新结果提供了对游戏中角色互动的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How bounded rationality of individuals in social interactions impacts evolutionary dynamics of cooperation
In this study, we explore the emergence of cooperative behavior in the prisoner's dilemma evolutionary game. In particular, we investigate the effect of bounded rationality of individuals on the networking topology (i.e., the individuals' personal networks). For this, we highlight the evolutionary dynamics of cooperation on top of different graph topologies with respect to their baseline properties such as average shortest path length and clustering coefficient. In addition, we test the effect of a new variable, called memory of interactions, on the changes in behavior and decision-making of the players as well as the networking outcome. For this purpose, we use agent-based modeling, which allows studying how changes in the environment or changes of properties of networked actors affect the evolutionary dynamics of cooperation among them. The results of our analysis confirm that the networking topology and the memory duration are important in affecting the emergence of cooperative behavior of players. They also impact the total utility that can be obtained from playing the Prisoner's Dilemma evolutionary game. Although the Prisoner's Dilemma game simulations tend towards full cooperation, if they are run over graph topologies with short average shortest path lengths and low clustering coefficients, the number of steps needed to reach equilibrium increases. This new result provides an understanding of the interactions of actors in a game.
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