The autonomous selection mechanism on higher-order networks promotes group cooperation

Jiaoyuan Wang and Yanlong Yang

Chaos, Solitons & Fractals, 2026, vol. 210, issue P2

Abstract: Higher-order networks well characterize multi-agent group interactions in reality, yet traditional models adopt fixed structures that neglect the autonomy of individuals to choose interaction groups. To fill this gap, we propose an autonomous selection mechanism that enables agents to leave and reorganize low-payoff groups for higher payoffs. Simulation and analysis demonstrate that this mechanism effectively promotes the overall cooperation rate and group payoffs, thereby breaking the deadlock of full defection. We classify the mechanism into local and global selection, and demonstrate that local selection is superior in cooperation promotion and parameter robustness, owing to the incorrect pairing cost inherent in autonomous selection. We also find that global selection exacerbates free-riding behavior in public goods games within mixed-game settings. Additionally, we propose a constructed higher-order network generation method and a new visualization scheme. Finally, based on the results of the theoretical analysis, the robustness of this model is demonstrated. This work reveals that restricted, local autonomous selection is more effective for cooperation than unrestricted global selection, providing new insights into the evolution of collective cooperation in complex higher-order systems.

Keywords: Higher-order networks; Autonomous selection; Evolutionary game; Cooperation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:210:y:2026:i:p2:s0960077926008362

DOI: 10.1016/j.chaos.2026.118695

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