Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents
Name
pnas.2106028118.pdf
Description
Published version
Size
1.83 MB
Format
Adobe PDF
Checksum (MD5)
418796860b42a73a0496608b049de097
Author(s)
Köster, Raphael
Hadfield-Menell, Dylan
Everett, Richard
Weidinger, Laura
Hadfield, Gillian K
Leibo, Joel Z
Date Issued
2022
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
Abstract
Significance
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
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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DOI of Published Version
https://doi.org/10.1073/PNAS.2106028118
