Strategy evolution on temporal hypergraphs

Individuals interact and cooperate in structured systems. Many studies represent this structure using static networks, where each link represents a permanent connection between two nodes. However, real interactions are generally not time-invariant and are often not pairwise. Recently, progress has been made in modeling higher-order interactions using hypergraphs, where a link may connect more than two individuals. Here, we study cooperation on temporal hypergraphs, capturing the time-varying, higher-order interactions seen in empirical systems. We find that temporal hypergraphs can promote cooperation compared with static networks, and the latter may even underestimate the cooperation-boosting effects of constrained, local interactions. We further show that cooperation can be facilitated by temporal hypergraphs with sparse components and higher-order interactions. Importantly, when the size of group interactions (hyperedges) is comparable to the population size, relatively small hyperedge sizes best facilitate cooperation. Synthetic and empirical hypergraphs alike affirm our findings, illuminating how temporal, higher-order interactions profoundly shape the evolution of cooperation.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-02-12
DOI
https://doi.org/10.1073/pnas.2516380123
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
0.00

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article

Strategy evolution on temporal hypergraphs

Alex McAvoy, Aming Li, Lei Zhou, Xiaochen Wang
Proceedings of the National Academy of Sciences
Evolutionary Game Theory and Cooperation
article

Strategy evolution on temporal hypergraphs

Alex McAvoy, Aming Li, Lei Zhou, Xiaochen Wang
article en

Abstract

Individuals interact and cooperate in structured systems. Many studies represent this structure using static networks, where each link represents a permanent connection between two nodes. However, real interactions are generally not time-invariant and are often not pairwise. Recently, progress has been made in modeling higher-order interactions using hypergraphs, where a link may connect more than two individuals. Here, we study cooperation on temporal hypergraphs, capturing the time-varying, higher-order interactions seen in empirical systems. We find that temporal hypergraphs can promote cooperation compared with static networks, and the latter may even underestimate the cooperation-boosting effects of constrained, local interactions. We further show that cooperation can be facilitated by temporal hypergraphs with sparse components and higher-order interactions. Importantly, when the size of group interactions (hyperedges) is comparable to the population size, relatively small hyperedge sizes best facilitate cooperation. Synthetic and empirical hypergraphs alike affirm our findings, illuminating how temporal, higher-order interactions profoundly shape the evolution of cooperation.

Proceedings of the National Academy of SciencesVol. 123(7)
Beijing Institute of Technology (CN), Peking University (CN), Robotics Research (United States) (US), Artificial Intelligence in Medicine (Canada) (CA), Toronto Metropolitan University (CA)
National Natural Science Foundation of China, Beijing Institute of Technology, Peking University, Beijing Institute of Technology Research Fund Program for Young Scholars, University of North Carolina at Chapel Hill, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Partnerships for the goals
Openalex Percentile: Top 62%
Evolutionary Game Theory and Cooperation
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Strategy evolution on temporal hypergraphs — Alex McAvoy, Aming Li, et al. · Proceedings of the National Academy of Sciences (2026) | TGRS Research Map | TGRS