| dc.contributor.author | Köster, Raphael | |
| dc.contributor.author | Hadfield-Menell, Dylan | |
| dc.contributor.author | Everett, Richard | |
| dc.contributor.author | Weidinger, Laura | |
| dc.contributor.author | Hadfield, Gillian K | |
| dc.contributor.author | Leibo, Joel Z | |
| dc.date.accessioned | 2022-06-22T17:53:52Z | |
| dc.date.available | 2022-06-22T17:53:52Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://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.iso | en | |
| dc.publisher | Proceedings of the National Academy of Sciences | en_US |
| dc.relation.isversionof | 10.1073/PNAS.2106028118 | en_US |
| dc.rights | 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. | en_US |
| dc.source | PNAS | en_US |
| dc.title | Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents | en_US |
| dc.type | Article | en_US |
| dc.identifier.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). | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
| dc.relation.journal | Proceedings of the National Academy of Sciences of the United States of America | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2022-06-22T17:10:14Z | |
| dspace.orderedauthors | Köster, R; Hadfield-Menell, D; Everett, R; Weidinger, L; Hadfield, GK; Leibo, JZ | en_US |
| dspace.date.submission | 2022-06-22T17:10:16Z | |
| mit.journal.volume | 119 | en_US |
| mit.journal.issue | 3 | en_US |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |