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Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents

Author(s)
Köster, Raphael; Hadfield-Menell, Dylan; Everett, Richard; Weidinger, Laura; Hadfield, Gillian K; Leibo, Joel Z; ... Show more Show less
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
<jats:title>Significance</jats:title> <jats:p>The fact that humans enforce and comply with norms is an important reason why humans enjoy higher levels of cooperation and welfare than other animals. Some norms are relatively easy to explain: They may prohibit obviously harmful or uncooperative actions. But many norms are not easy to explain. For example, most cultures prohibit eating certain kinds of foods, and almost all societies have rules about what constitutes appropriate clothing, language, and gestures. Using a computational model focused on learning shows that apparently pointless rules can have an indirect effect on welfare. They can help agents learn how to enforce and comply with norms in general, improving the group’s ability to enforce norms that have a direct effect on welfare.</jats:p>
Date issued
2022
URI
https://hdl.handle.net/1721.1/143542
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Köster, Raphael, Hadfield-Menell, Dylan, Everett, Richard, Weidinger, Laura, Hadfield, Gillian K et al. 2022. "Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents." Proceedings of the National Academy of Sciences of the United States of America, 119 (3).
Version: Final published version

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