Evolutionary cooperation under collective risk on hypergraphs

Understanding how cooperation emerges and persists in complex systems remains a central challenge in evolutionary game theory. While prior research has predominantly focused on pairwise interactions, real-world societies and ecosystems are often shaped by group interactions that are inherently higher-order. Here, we investigate the evolution of cooperation in a threshold public goods game with collective risk, implemented on uniform random hypergraph. In this setting, individuals interact within hyperedges, and cooperation yields benefits only when the collective contribution exceeds a critical threshold; otherwise, all participants face the risk of losing their endowments. Using numerical simulations and analytical approximations based on replicator dynamics, we show that collective risk substantially promotes cooperation. Notably, a moderate risk threshold leads to the most pronounced enhancement of cooperative behavior. By tracking the distribution of cooperators across all hyperedges, we find that collective risk increases the resilience of highly cooperative groups to defector exploitation, while simultaneously reducing the prevalence of low-cooperation groups. This structural shift underpins the mechanism by which collective risk fosters cooperation. Finally, we validate the robustness of this mechanism by varying key structural parameters of the hypergraph, further confirming the effectiveness of collective risk in sustaining cooperation under high-order interaction networks.

Authors

Institutions

Publication Details

Journal
Chaos Solitons & Fractals
Published
2025-07-21
DOI
https://doi.org/10.1016/j.chaos.2025.116859
Citations
3
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
12.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evolutionary cooperation under collective risk on hypergraphs

Wenhua Li, Yewei Tao, Лей Ши, Kaipeng Hu et al.
3 citations
Chaos Solitons & Fractals
Evolutionary Game Theory and Cooperation
12.60
article

Evolutionary cooperation under collective risk on hypergraphs

Wenhua Li, Yewei Tao, Лей Ши, Kaipeng Hu, Xiaoqian Zhao
article en
3 citations

Abstract

Understanding how cooperation emerges and persists in complex systems remains a central challenge in evolutionary game theory. While prior research has predominantly focused on pairwise interactions, real-world societies and ecosystems are often shaped by group interactions that are inherently higher-order. Here, we investigate the evolution of cooperation in a threshold public goods game with collective risk, implemented on uniform random hypergraph. In this setting, individuals interact within hyperedges, and cooperation yields benefits only when the collective contribution exceeds a critical threshold; otherwise, all participants face the risk of losing their endowments. Using numerical simulations and analytical approximations based on replicator dynamics, we show that collective risk substantially promotes cooperation. Notably, a moderate risk threshold leads to the most pronounced enhancement of cooperative behavior. By tracking the distribution of cooperators across all hyperedges, we find that collective risk increases the resilience of highly cooperative groups to defector exploitation, while simultaneously reducing the prevalence of low-cooperation groups. This structural shift underpins the mechanism by which collective risk fosters cooperation. Finally, we validate the robustness of this mechanism by varying key structural parameters of the hypergraph, further confirming the effectiveness of collective risk in sustaining cooperation under high-order interaction networks.

Chaos Solitons & FractalsVol. 199
Shanghai Lixin University of Accounting and Finance (CN), Yunnan University of Finance And Economics (CN)
Partnerships for the goals
Openalex Percentile: Top 2%
Evolutionary Game Theory and Cooperation
12.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Evolutionary cooperation under collective risk on hypergraphs — Wenhua Li, Yewei Tao, et al. · Chaos Solitons & Fractals (2025) | TGRS Research Map | TGRS