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高阶网络的策略进化。

Strategy evolution on higher-order networks.

机构信息

Center for Systems and Control, College of Engineering, Peking University, Beijing, China.

Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

Nat Comput Sci. 2024 Apr;4(4):274-284. doi: 10.1038/s43588-024-00621-8. Epub 2024 Apr 15.

Abstract

Cooperation is key to prosperity in human societies. Population structure is well understood as a catalyst for cooperation, where research has focused on pairwise interactions. But cooperative behaviors are not simply dyadic, and they often involve coordinated behavior in larger groups. Here we develop a framework to study the evolution of behavioral strategies in higher-order population structures, which include pairwise and multi-way interactions. We provide an analytical treatment of when cooperation will be favored by higher-order interactions, accounting for arbitrary spatial heterogeneity and nonlinear rewards for cooperation in larger groups. Our results indicate that higher-order interactions can act to promote the evolution of cooperation across a broad range of networks, in public goods games. Higher-order interactions consistently provide an advantage for cooperation when interaction hyper-networks feature multiple conjoined communities. Our analysis provides a systematic account of how higher-order interactions modulate the evolution of prosocial traits.

摘要

合作是人类社会繁荣的关键。人口结构被很好地理解为合作的催化剂,研究集中在 两两相互作用上。但是合作行为不仅仅是二元的,它们通常涉及更大群体中的协调行为。在这里,我们开发了一个框架来研究在包括两两和多向相互作用的高阶人口结构中行为策略的进化。我们提供了一种分析处理方法,用于何时高阶相互作用将有利于合作,考虑了任意空间异质性和更大群体中合作的非线性奖励。我们的结果表明,高阶相互作用可以在广泛的网络中促进合作的进化,在公共物品博弈中就是如此。当相互作用超网络具有多个连接的社区时,高阶相互作用始终为合作提供优势。我们的分析提供了一个系统的说明,即高阶相互作用如何调节亲社会特征的进化。

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