多层网络中的合作演化
Evolution of cooperation in the multiplex
AI总结：
基于多层网络中的多表型同质性，推导了自然选择促进合作的分析条件，揭示了表型多样性通过划分同配生态位促进合作，并发现囚徒困境的强度改变合作对策略突变的依赖性。
AI中文摘要：
在生物和社会系统中，合作通常依赖于表型线索而非随机相遇。为了解释在多个同时维度上展开的真实世界互动，我们在此开发了一个由多表型同质性支配的多层网络中合作演化的通用框架。我们推导了自然选择有利于在独立或具有上位性的表型性状以及不同突变耦合模式下合作的分析条件。尽管适应度跨层整合，合作的条件解析为层特定的$σ$-规则，仅依赖于局部收益结构、有效表型数量和突变率。我们表明，表型多样性通过将种群划分为同配生态位来促进合作。此外，在有限种群中，囚徒困境的加剧使合作对策略突变的依赖性从单调递减，经过U形，变为单调递增。我们的工作为多表型同质性如何支撑异质种群中合作的演化动态提供了统一解释。
英文摘要：
Across biological and social systems, cooperation often depends on phenotypic cues rather than random encounters. To account for real-world interactions unfolding across multiple, simultaneous dimensions, here we develop a general framework for the evolution of cooperation in multiplex networks governed by multi-phenotype homophily. We derive analytical conditions for natural selection to favor cooperation across phenotypic traits that are independent or exhibit epistasis and under different modes of mutation coupling. Despite the integration of fitness across layers, the conditions for cooperation resolve into layer-specific $σ$-rules, depending only on the local payoff structure, the effective number of phenotypes, and the mutation rates. We show that phenotypic diversity fosters cooperation by partitioning populations into assortative niches. Furthermore, in finite populations, intensifying the prisoner's dilemma shifts the dependence of cooperation on strategy mutation from monotonically decreasing, through U-shaped, to monotonically increasing. Our work provides a unified account of how multi-phenotype homophily underpins the evolutionary dynamics of cooperation in heterogeneous populations.
