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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,5,19]]},"abstract":"<jats:p>People make strategic decisions many times a day\u2014during negotiations, when coordinating actions with others, or when choosing partners for cooperation. The resulting dynamics can be studied with learning theory and evolutionary game theory. These frameworks explore how people adapt their decisions over time, in light of how effective their strategies have been. The outcomes of such learning processes depend on how sensitive individuals are to the performance of their strategies. When they are more sensitive, they systematically favor strategies they deem more successful. When they are less sensitive, their learning process is noisier and more erratic. Traditionally, most models treat this sensitivity as a fixed parameter\u2014like the \u201cselection strength\u201d parameter in evolutionary models. Instead, we study how strategies and sensitivities coevolve. We find that the coevolutionary endpoints depend on both the type of strategic interaction and the learning rule employed. In prisoner\u2019s dilemmas, we often observe sensitivities to increase indefinitely. But in snowdrift and stag-hunt games, sensitivities often converge to a finite value, or we observe evolutionary branching altogether. These results shed light on how evolution might shape learning mechanisms for social behavior. They suggest that noisy learning does not need to be a by-product of cognitive constraints. Instead, it can serve as a means to gain strategic advantages.<\/jats:p>","DOI":"10.1073\/pnas.2529959123","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:59:20Z","timestamp":1778608760000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":1,"title":["Evolution of noisy learning in games"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2868-9685","authenticated-orcid":false,"given":"Marta C.","family":"Couto","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04dkp9463","id-type":"ROR","asserted-by":"publisher"}],"name":"Socially Intelligent Artificial Systems Group, Informatics Institute, University of Amsterdam","place":["Amsterdam, The Netherlands"]},{"id":[{"id":"https:\/\/ror.org\/0534re684","id-type":"ROR","asserted-by":"publisher"}],"name":"Max Planck Research Group on the Dynamics of Social Behavior, Max Planck Institute for Evolutionary Biology","place":["Pl\u00f6n, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernando P.","family":"Santos","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04dkp9463","id-type":"ROR","asserted-by":"publisher"}],"name":"Socially Intelligent Artificial Systems Group, Informatics Institute, University of Amsterdam","place":["Amsterdam, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5116-955X","authenticated-orcid":false,"given":"Christian","family":"Hilbe","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0534re684","id-type":"ROR","asserted-by":"publisher"}],"name":"Max Planck Research Group on the Dynamics of Social Behavior, Max Planck Institute for Evolutionary Biology","place":["Pl\u00f6n, Germany"]},{"id":[{"id":"https:\/\/ror.org\/03jzk4720","id-type":"ROR","asserted-by":"publisher"}],"name":"Interdisciplinary Transformation University","place":["Linz, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.092080099"},{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1109672110"},{"key":"e_1_3_4_3_2","volume-title":"The Theory of Learning in Games","author":"Fudenberg D.","year":"1998","unstructured":"D. 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