Cooperation is encouraged simply by increasing the number of participants

Cooperation is encouraged simply by increasing the number of participants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cooperation is encouraged simply by increasing the number of participants Kazuhiro Tamura, Satoru Morita This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3695019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Humans have occasionally exhibited cooperative behaviour, deviating from individual rationality, in experiments such as the public goods game and prisoner’s dilemma. Despite numerous experiments, the alignment between human cooperative behaviour and game theory predictions remains inconsistent. Although comprehending human cooperation through experimentation is pivotal, large-scale experiments with human subjects pose challenges, resulting in insufficient data on cooperative behaviour across diverse populations. Here, we present a new approach using Deep Q-Learning-based agents—a type of artificial intelligence—in a public goods game, revealing an intriguing trend, specifically that agents increasingly opt for cooperation as the number of participants rises. This viewpoint challenges prevailing research paradigms by underscoring the importance of group size—an aspect hitherto underexplored. Additionally, this approach mitigates experimental costs, enhancing scalability and eliminating the influence of the details of experimental design. Anticipated outcomes include demonstrating sustained cooperation even in scenarios dominated by non-cooperation in established game theory, thereby narrowing the gap between theoretical predictions and experimental observations. These findings hold potential applications, such as attracting substantial investments for large-scale public projects. Additionally, implementing a mechanism in social networking systems that integrates Deep Q-Learning agents into communities may mitigate fragmentation by promoting cooperative behaviour amongst diverse participants. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialFigure2aalpha0.25.csv SupplementarymaterialFigure2balpha0.40.csv SupplementarymaterialFigure2calpha0.50.csv SupplementarymaterialFigure2dalpha0.60.csv SupplementarymaterialFigure3an3.csv SupplementarymaterialFigure3bn5.csv SupplementarymaterialFigure3cn10.csv SupplementarymaterialFigure3dn20.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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