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.
Authors
- Alex McAvoy
- Aming Li
- Lei Zhou
- Xiaochen Wang
Institutions
- Beijing Institute of Technology (CN)
- Peking University (CN)
- Robotics Research (United States) (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- Toronto Metropolitan University (CA)
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
Funders
- 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