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dc.contributor.authorKöster, Raphael
dc.contributor.authorHadfield-Menell, Dylan
dc.contributor.authorEverett, Richard
dc.contributor.authorWeidinger, Laura
dc.contributor.authorHadfield, Gillian K
dc.contributor.authorLeibo, Joel Z
dc.date.accessioned2022-06-22T17:53:52Z
dc.date.available2022-06-22T17:53:52Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/143542
dc.description.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>en_US
dc.language.isoen
dc.publisherProceedings of the National Academy of Sciencesen_US
dc.relation.isversionof10.1073/PNAS.2106028118en_US
dc.rightsArticle 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.en_US
dc.sourcePNASen_US
dc.titleSpurious normativity enhances learning of compliance and enforcement behavior in artificial agentsen_US
dc.typeArticleen_US
dc.identifier.citationKöster, 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).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-06-22T17:10:14Z
dspace.orderedauthorsKöster, R; Hadfield-Menell, D; Everett, R; Weidinger, L; Hadfield, GK; Leibo, JZen_US
dspace.date.submission2022-06-22T17:10:16Z
mit.journal.volume119en_US
mit.journal.issue3en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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