Structure-aware imitation dynamics on higher-order networks

TL;DR

This paper introduces structure-aware imitation rules on hypergraphs, revealing how information diversity in social sampling influences cooperation in complex group interactions, supported by analytical and simulation results.

Contribution

It develops a new class of imitation rules on hypergraphs, linking social information diversity to cooperation success, and provides a unifying metric for higher-order network dynamics.

Findings

Higher information diversity promotes cooperation.

Analytical condition for cooperation success derived.

Principle extends to non-homogeneous hypergraphs.

Abstract

Imitation is a basic updating mechanism for strategy evolution in structured populations, determining how individuals sample social information and translate it into behavioral changes. Higher-order networks, such as hypergraphs, generalize pairwise links to hyperedges and provide a natural representation of group interactions. Yet existing studies on higher-order networks largely emphasize structural effects, while the impact of imitation-based update rules and how they interact with group structures remains poorly understood. Here, we introduce a class of structure-aware imitation rules on hypergraphs that explicitly parameterize how many groups are sampled and how many peers are consulted within each sampled group. Under weak selection, we derive an analytical condition for the success of cooperation for any multiplayer social dilemmas on homogeneous hypergraphs. This analysis yields an interpretable metric, information diversity, which quantifies how an update rule diversifies the sources of social information across groups. Analytical predictions and numerical simulations show that cooperation is more effectively promoted by update rules that induce higher information diversity for three representative dilemmas. Further simulations demonstrate that this principle extends to non-homogeneous hypergraphs and a broad class of multiplayer social dilemmas. Our work thus provides a unifying metric that links microscopic updating to evolutionary outcomes in higher-order networked systems and establishes a general design principle for promoting cooperation beyond pairwise interactions.