Evolutionary dynamics of pairwise and group cooperation in heterogeneous social networks
Dini Wang, Peng Yi, Gang Yan, Feng Fu

TL;DR
This paper introduces a framework for analyzing cooperation in weighted social networks, revealing that degree-inverse tie weights promote cooperation and outperform homogeneous networks, with implications for biological and social systems.
Contribution
The study develops a quenched mean-field framework for weighted networks and demonstrates that degree-inverse tie weights enhance cooperation in diverse network structures.
Findings
Degree-inverse weighted ties promote cooperation in both pairwise and group dilemmas.
Heterogeneous networks with degree-inverse weights outperform homogeneous ones in cooperation fixation.
The approach is validated on 30,000 random and 13 real-world networks.
Abstract
Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties -- reinforcing tie strengths between peripheral nodes while weakening those between hubs -- robustly promote cooperation in both pairwise and group dilemmas. Importantly, this…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Evolutionary Game Theory and Cooperation · Complex Network Analysis Techniques
