Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines
Summary: High levels of human-machine cooperation are required to combine the strengths of human and artificial intelligence. Here, we investigate strategies to overcome the machine penalty, where people are less cooperative with partners they assume to be machines, than with partners they assume to...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225010946 |
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| author | Kinga Makovi Jean-François Bonnefon Mayada Oudah Anahit Sargsyan Talal Rahwan |
| author_facet | Kinga Makovi Jean-François Bonnefon Mayada Oudah Anahit Sargsyan Talal Rahwan |
| author_sort | Kinga Makovi |
| collection | DOAJ |
| description | Summary: High levels of human-machine cooperation are required to combine the strengths of human and artificial intelligence. Here, we investigate strategies to overcome the machine penalty, where people are less cooperative with partners they assume to be machines, than with partners they assume to be humans. Using a large-scale iterative public goods game with nearly 2,000 participants, we find that peer rewards or peer punishments can both promote cooperation with partners assumed to be machines but do not overcome the machine penalty. Their combination, however, eliminates the machine penalty, because it is uniquely effective for partners assumed to be machines and inefficient for partners assumed to be humans. These findings provide a nuanced road map for designing a cooperative environment for humans and machines, depending on the exact goals of the designer. |
| format | Article |
| id | doaj-art-0bfb62ec39d34a4db0b597c258901f61 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-0bfb62ec39d34a4db0b597c258901f612025-08-20T02:22:01ZengElsevieriScience2589-00422025-07-0128711283310.1016/j.isci.2025.112833Rewards and punishments help humans overcome biases against cooperation partners assumed to be machinesKinga Makovi0Jean-François Bonnefon1Mayada Oudah2Anahit Sargsyan3Talal Rahwan4Social Science Division, New York University Abu Dhabi, Abu Dhabi, UAE; Corresponding authorToulouse School of Economics, CNRS (TSM-R), University of Toulouse Capitole, Toulouse, FranceSocial Science Division, New York University Abu Dhabi, Abu Dhabi, UAESocial Science Division, New York University Abu Dhabi, Abu Dhabi, UAE; School of Social Sciences and Technology, Technical University of Munich, München, GermanyComputer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, UAE; Corresponding authorSummary: High levels of human-machine cooperation are required to combine the strengths of human and artificial intelligence. Here, we investigate strategies to overcome the machine penalty, where people are less cooperative with partners they assume to be machines, than with partners they assume to be humans. Using a large-scale iterative public goods game with nearly 2,000 participants, we find that peer rewards or peer punishments can both promote cooperation with partners assumed to be machines but do not overcome the machine penalty. Their combination, however, eliminates the machine penalty, because it is uniquely effective for partners assumed to be machines and inefficient for partners assumed to be humans. These findings provide a nuanced road map for designing a cooperative environment for humans and machines, depending on the exact goals of the designer.http://www.sciencedirect.com/science/article/pii/S2589004225010946Artificial intelligenceSocial sciences |
| spellingShingle | Kinga Makovi Jean-François Bonnefon Mayada Oudah Anahit Sargsyan Talal Rahwan Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines iScience Artificial intelligence Social sciences |
| title | Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| title_full | Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| title_fullStr | Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| title_full_unstemmed | Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| title_short | Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| title_sort | rewards and punishments help humans overcome biases against cooperation partners assumed to be machines |
| topic | Artificial intelligence Social sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225010946 |
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