Modelling Behaviour in Uncertainty: A Simulation Study of Heads-Up Poker.

Modelling Behaviour in Uncertainty: A Simulation Study of Heads-Up Poker. | 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 Modelling Behaviour in Uncertainty: A Simulation Study of Heads-Up Poker. Sagnik Chakraborty, Sourjya Sarkar, Sayan Saha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6015303/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 Modelling human behaviour in situations of incomplete information is a domain with significant implications for the development of AI, empirically understanding facets of psychology, decision theory, sociology, and economics. Due to the suitability of poker to the structure of this particular problem, this study employs the game to propose a model for human behaviour using a probability distribution over the array of possible decisions, incorporating parameters that reflect real characteristics: confidence(⍺), risk appetite(β), and bluff frequency. It uses different combinations of these parameters to run Monte-Carlo simulations of heads-up poker games to understand what makes a particular strategy successful. The simulations revealed that strategies with slightly higher confidence and higher risk appetite yielded the best results in situations closer to reality, while slight underconfidence and lower risk appetite fared better against an "ideal" opponent who has no biases. The proposed model offers a computationally efficient tool for deep learning-based poker AI, with applications in opponent modelling, behavioural economics, and agent-based modelling. Behavioral Economics Decision Sciences Artificial Intelligence and Machine Learning Poker Human Behaviour Modelling Incomplete Information Decision Theory Monte Carlo Simulation Artificial Intelligence Full Text Additional Declarations The authors declare no competing interests. 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-6015303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414777636,"identity":"c62b1cfb-b157-4bea-9e6b-d1a70572c13e","order_by":0,"name":"Sagnik 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