Human adaptation to adaptive machines converges to game-theoretic equilibria
Abstract Here we test three learning algorithms for machines playing general-sum games with human subjects. The algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine l...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12998-1 |
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| author | Benjamin J. Chasnov Lillian J. Ratliff Samuel A. Burden |
| author_facet | Benjamin J. Chasnov Lillian J. Ratliff Samuel A. Burden |
| author_sort | Benjamin J. Chasnov |
| collection | DOAJ |
| description | Abstract Here we test three learning algorithms for machines playing general-sum games with human subjects. The algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine learning algorithms work directly from observations of human actions without solving an inverse problem to estimate the human’s utility function as in prior work. Surprisingly, one algorithm can steer the human-machine interaction to the machine’s optimum, effectively controlling the human’s actions even while the human responds optimally to their perceived cost landscape. Our results show that game theory can be used to predict and design outcomes of co-adaptive interactions between intelligent humans and machines. |
| format | Article |
| id | doaj-art-4914a2fdf23a404f8472c7684a5fdf36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4914a2fdf23a404f8472c7684a5fdf362025-08-20T04:01:52ZengNature PortfolioScientific Reports2045-23222025-08-0115111010.1038/s41598-025-12998-1Human adaptation to adaptive machines converges to game-theoretic equilibriaBenjamin J. Chasnov0Lillian J. Ratliff1Samuel A. Burden2Department of Electrical & Computer Engineering, University of WashingtonDepartment of Electrical & Computer Engineering, University of WashingtonDepartment of Electrical & Computer Engineering, University of WashingtonAbstract Here we test three learning algorithms for machines playing general-sum games with human subjects. The algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine learning algorithms work directly from observations of human actions without solving an inverse problem to estimate the human’s utility function as in prior work. Surprisingly, one algorithm can steer the human-machine interaction to the machine’s optimum, effectively controlling the human’s actions even while the human responds optimally to their perceived cost landscape. Our results show that game theory can be used to predict and design outcomes of co-adaptive interactions between intelligent humans and machines.https://doi.org/10.1038/s41598-025-12998-1human-AI interactionmulti-agent systemsgame theory |
| spellingShingle | Benjamin J. Chasnov Lillian J. Ratliff Samuel A. Burden Human adaptation to adaptive machines converges to game-theoretic equilibria Scientific Reports human-AI interaction multi-agent systems game theory |
| title | Human adaptation to adaptive machines converges to game-theoretic equilibria |
| title_full | Human adaptation to adaptive machines converges to game-theoretic equilibria |
| title_fullStr | Human adaptation to adaptive machines converges to game-theoretic equilibria |
| title_full_unstemmed | Human adaptation to adaptive machines converges to game-theoretic equilibria |
| title_short | Human adaptation to adaptive machines converges to game-theoretic equilibria |
| title_sort | human adaptation to adaptive machines converges to game theoretic equilibria |
| topic | human-AI interaction multi-agent systems game theory |
| url | https://doi.org/10.1038/s41598-025-12998-1 |
| work_keys_str_mv | AT benjaminjchasnov humanadaptationtoadaptivemachinesconvergestogametheoreticequilibria AT lillianjratliff humanadaptationtoadaptivemachinesconvergestogametheoreticequilibria AT samuelaburden humanadaptationtoadaptivemachinesconvergestogametheoreticequilibria |