{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:56Z","timestamp":1758672896394,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas.\n\nWe extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to align individual and collective interests.\n\nWe demonstrate our method on three environments from the Melting Pot suite, representing either common-pool resources or public goods.\n\nOur results illustrate how reward exchange can enable agents to transition from selfish to collective equilibria in a Markov social dilemma.\n\nThis work contributes to multi-agent reinforcement learning by providing a practical tool for analysing complex, multistep social dilemmas.\n\nOur findings offer insights into how reward structures can promote or hinder cooperation, with potential applications in areas such as mechanism design.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/33","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"284-292","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying the Self-Interest Level of Markov Social Dilemmas"],"prefix":"10.24963","author":[{"given":"Richard","family":"Willis","sequence":"first","affiliation":[{"name":"King's College London"}]},{"given":"Yali","family":"Du","sequence":"additional","affiliation":[{"name":"King's College London"}]},{"given":"Joel Z.","family":"Leibo","sequence":"additional","affiliation":[{"name":"King's College London"},{"name":"Google DeepMind"}]},{"given":"Michael","family":"Luck","sequence":"additional","affiliation":[{"name":"University of Sussex"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:32:39Z","timestamp":1758627159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/33"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/33","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}