Do LLMs Possess Strategic Intelligence? Testing LLMs in Iterated Prisoner’s Dilemmas

Do LLMs Possess Strategic Intelligence? Testing LLMs in Iterated Prisoner’s Dilemmas | 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 Research Article Do LLMs Possess Strategic Intelligence? Testing LLMs in Iterated Prisoner’s Dilemmas Jesse Kommandeur, Maarten Vonk, Stephan De Spiegeleire, Tim Sweijs This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405969/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 This study systematically evaluates the strategic capabilities of large language models through evolutionary tournaments in the Iterated Prisoner's Dilemma. We tested 28 agents, including 12 contemporary large language models from four major providers (OpenAI, Anthropic, Google, Mistral), 13 canonical game theory strategies, and 3 adaptive learning algorithms across varying interaction horizons, temperatures, and memory conditions. The experimental framework employed five-phase evolutionary tournaments with fitness-proportional selection, examining both anonymous memory and opponent tracking mechanisms to investigate how historical information impacts strategic behaviour. Results reveal that most large language models maintained high cooperation rates even in short-horizon scenarios where game theory predicts universal defection, with only GPT-5-mini demonstrating sophisticated horizon-sensitive strategic adaptation. To deepen understanding, future evaluations should include longer games, deeper tournaments, expanded cross-model comparisons, and richer environmental variations to more reliably characterise strategic behaviour across horizons and evolutionary depth. Game Theory Iterated Prisoner’s Dilemma Large Language Models Strategic Intelligence Multi-Agent Systems Evolutionary Dynamics Full Text Additional Declarations No competing interests reported. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8405969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571730696,"identity":"a63c2437-e516-46c1-9c5c-269fa0583b24","order_by":0,"name":"Jesse 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We tested 28 agents, including 12 contemporary large language models from four major providers (OpenAI, Anthropic, Google, Mistral), 13 canonical game theory strategies, and 3 adaptive learning algorithms across varying interaction horizons, temperatures, and memory conditions. The experimental framework employed five-phase evolutionary tournaments with fitness-proportional selection, examining both anonymous memory and opponent tracking mechanisms to investigate how historical information impacts strategic behaviour. Results reveal that most large language models maintained high cooperation rates even in short-horizon scenarios where game theory predicts universal defection, with only GPT-5-mini demonstrating sophisticated horizon-sensitive strategic adaptation. 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