Emergence of cooperation promoted by higher-order strategy updates
Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the group-mutual comparison update - selecting a high-fitness group and then imitating a random individual within this group - can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems.
Authors
- Dini Wang
- Peng Yi (ORCID: https://orcid.org/0000-0002-2494-1505)
- Yiguang Hong (ORCID: https://orcid.org/0000-0001-9505-8739)
- Jie Chen (ORCID: https://orcid.org/0000-0003-3171-1887)
- Gang Yan (ORCID: https://orcid.org/0000-0003-0141-8102)
Institutions
- Tongji University (CN)
- Intelligent Systems Research (United States) (US)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1371/journal.pcbi.1012891
- Citations
- 4
- Primary Topic
- Evolutionary Game Theory and Cooperation
- Type
- article
- Field-Weighted Citation Impact
- 16.81
Funders
- National Natural Science Foundation of China
- Key Technologies Research and Development Program