噪声下间接互易演化稳定性的确切条件。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Nikoleta E Glynatsi, Christian Hilbe, Yohsuke Murase
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引用次数: 0

摘要

间接互惠是大规模合作的关键机制。这种机制抓住了人们帮助他人建立和维持良好声誉的部分洞察力。为了进行这种合作,适当的社会规范是必不可少的。它们规定了个人应该如何根据彼此的声誉行事,以及声誉如何根据个人行为而更新。虽然以前的工作已经确定了几个维持合作的规范,但仍然缺乏对所有进化稳定规范的完整分析特征,特别是当评估或行动嘈杂时。在这项研究中,我们为公共评估制度提供了这样一个特征。这种特征再现了已知的结果,例如最主要的八个规范,但它扩展到更一般的情况,允许各种类型的错误和额外的行动,包括代价高昂的惩罚。我们还确定了对任何突变策略施加固定收益的规范,类似于直接互惠中的零决定策略。这些结果为理解合作通过间接互惠的演变和社会规范的关键作用提供了严格的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exact conditions for evolutionary stability in indirect reciprocity under noise.

Indirect reciprocity is a key mechanism for large-scale cooperation. This mechanism captures the insight that in part, people help others to build and maintain a good reputation. To enable such cooperation, appropriate social norms are essential. They specify how individuals should act based on each others' reputations, and how reputations are updated in response to individual actions. Although previous work has identified several norms that sustain cooperation, a complete analytical characterization of all evolutionarily stable norms remains lacking, especially when assessments or actions are noisy. In this study, we provide such a characterization for the public assessment regime. This characterization reproduces known results, such as the leading eight norms, but it extends to more general cases, allowing for various types of errors and additional actions including costly punishment. We also identify norms that impose a fixed payoff on any mutant strategy, analogous to the zero-determinant strategies in direct reciprocity. These results offer a rigorous foundation for understanding the evolution of cooperation through indirect reciprocity and the critical role of social norms.

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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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