多路网络中多通道博弈的合作演化。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2024-12-19 eCollection Date: 2024-12-01 DOI:10.1371/journal.pcbi.1012678
Amit Basak, Supratim Sengupta
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引用次数: 0

摘要

人类驾驭着不同的社会关系,同时在多种社会环境中互动。一个人在一个环境中的行为可以影响在其他环境中的行为。在不同领域中,与互动相关的不同回报激发了最近对合作进化的研究,通过对多渠道博弈的分析,每个个体同时参与多个重复博弈。然而,以往的研究忽略了网络结构在各个领域的潜在作用,以及在不同领域对抗不同的相互作用伙伴的影响。多路网络提供了一个有用的框架来表示跨不同社会背景的同一组代理之间的社会互动。研究了多通道博弈中多路网络结构和策略连接对多路网络各层合作行为传播的影响。我们发现,在各层网络结构相同的情况下,与囚徒困境博弈中混合良好的群体相比,多路复用结构和策略链接提高了多路复用各层的合作率。策略链接在促进合作方面的有效性取决于网络结构的相似程度和由于记忆不完全而产生的感知误差。当不同层次的结构重叠程度足够大,并且感知误差的概率相对较低时,可以获得较高的合作率。我们的工作揭示了多元社会中不同层次的社会网络结构是如何通过限制个体在不同社会领域之间联系策略的能力来影响合作的传播的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolution of cooperation in multichannel games on multiplex networks.

Humans navigate diverse social relationships and concurrently interact across multiple social contexts. An individual's behavior in one context can influence behavior in other contexts. Different payoffs associated with interactions in the different domains have motivated recent studies of the evolution of cooperation through the analysis of multichannel games where each individual is simultaneously engaged in multiple repeated games. However, previous investigations have ignored the potential role of network structure in each domain and the effect of playing against distinct interacting partners in different domains. Multiplex networks provide a useful framework to represent social interactions between the same set of agents across different social contexts. We investigate the role of multiplex network structure and strategy linking in multichannel games on the spread of cooperative behavior in all layers of the multiplex. We find that multiplex structure along with strategy linking enhances the cooperation rate in all layers of the multiplex compared to a well-mixed population in Prisoners' Dilemma games, provided the network structure is identical across layers. The effectiveness of strategy linking in enhancing cooperation depends on the degree of similarity of the network structure across the layers and perception errors due to imperfect memory. Higher cooperation rates are achieved when the degree of structural overlap of the different layers is sufficiently large, and the probability of perception error is relatively low. Our work reveals how the social network structure in different layers of a multiplex can affect the spread of cooperation by limiting the ability of individuals to link strategies across different social domains.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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