AI Insight
This study develops a mathematical framework to understand how cooperation evolves in complex social and biological systems where interactions occur across multiple dimensions simultaneously and individuals preferentially interact with others sharing similar traits. The researchers derive analytical conditions showing that cooperation can be favored by natural selection when populations are divided into assortative groups based on multiple phenotypic characteristics, with the evolutionary outcome depending on local payoff structures, phenotypic diversity, and mutation rates. The work demonstrates that phenotypic diversity promotes cooperation by creating distinct niches where like individuals cluster together.
Why it matters
This framework provides theoretical insights into understanding cooperation in real-world scenarios from microbial communities to human social networks, where interactions are multidimensional rather than random. The findings could inform strategies for promoting cooperation in diverse settings including conservation biology, public health interventions, and social policy design.
arXiv:2606.00196v1 Announce Type: new
Abstract: 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 $sigma$-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.