Exploring cooperation mechanisms via deep reinforcement learning in network common-pool resource games

[2606.05867] Exploring cooperation mechanisms via deep reinforcement learning in network common-pool resource games Skip to main content Search Submit Donate Log in Computer Science > Computer Science and Game Theory arXiv:2606.05867 (cs) [Submitted on 4 Jun 2026 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Exploring cooperation mechanisms via deep reinforcement learning in network common-pool resource games Authors: Yihang Qin , Jinying Zou , Lin Wang View PDF Abstract: Sustaining cooperation in resource-constrained populations requires allocation mechanisms that balance individual incentives, resource sustainability, and distributional fairness. This paper proposes a network common-pool resource game in which individuals are embedded in complex networks, participate in multiple overlapping local resource pools, and face endogenous resource constraints during strategy evolution. Within this framework, we first examine two representative allocation mechanisms, equal allocation and proportional allocation. The results show that equal allocation produces fair but inefficient outcomes by weakening contribution incentives, whereas proportional allocation can temporarily promote cooperation but amplifies accumulated advantages and leads to severe inequality. To overcome these limitations, we develop a graph neural network-based reinforcement learning framework in which a learned social planner allocates local pool resources without directly controlling individual strategies. Simulation results under four representative network topologies show that the learned planner sustains higher cooperation levels and average accumulated resources, and reduces inequality compared with the baselines. Furthermore, we interpret the learned policy and distill it into two simpler mechanisms: a resource-dependent mixture mechanism for regular networks and a degree-conditioned mixture mechanism for heterogeneous networks. These mechanisms reveal that effective allocation should adapt to both local resource states and structural positions, providing an interpretable route from reinforcement learning policy search to mechanism design in networked resource-sharing systems. Comments: 28 pages, 10 figures, 3 tables Subjects: Computer Science and Game Theory (cs.GT) ; Dynamical Systems (math.DS); Physics and Society (physics.soc-ph) Cite as: arXiv:2606.05867 [cs.GT] (or arXiv:2606.05867v3 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2606.05867 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lin Wang [ view email ] [v1] Thu, 4 Jun 2026 08:44:01 UTC (16,485 KB) [v2] Sat, 27 Jun 2026 09:16:21 UTC (15,373 KB) [v3] Tue, 25 Aug 2026 02:25:19 UTC (5,668 KB) Full-text links: Access Paper: View PDF TeX Source view license Additional Features Audio Summary Current browse context: cs.GT < prev | next > new | recent | 2026-06 Change to browse by: cs math math.DS physics physics.soc-ph References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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