Fear of punishment promotes the emergence of cooperation and enhanced social welfare in social dilemmas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social punishment has been suggested as a key approach to ensuring high levels of cooperation and norm compliance in one-shot interactions. However, it has been shown that it only works when punishment is highly cost-efficient. On the other hand, signalling retribution hearkens back to medieval sovereignty, insofar as the very word for gallows in French stems from the Latin word for power and serves as a grim symbol of the ruthlessness of high justice. Here we introduce the mechanism of signalling an act of punishment and a special type of defector emerges, one who can recognise this signal and avoid punishment by way of fear. We perform extensive agent-based simulations so as to confirm and expand our understanding of the external factors that influence the success of social punishment. We show that our suggested mechanism serves as a catalyst for cooperation, even when signalling and punishment are very costly. We observe the preventive nature of advertising retributive acts and we contend that the resulting social prosperity is a desirable outcome in the contexts of AI and multi-agent systems. Overall, we suggest that fear acts as an effective stimulus to pro-social behaviour.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
PublisherAssociation for Computing Machinery (ACM)
Pages1819-1821
Number of pages3
ISBN (Electronic)9781450375184
Publication statusPublished - 9 May 2020
Event19th International Conference on Autonomous Agents and Multiagent Systems - Virtual, Auckland, New Zealand
Duration: 19 May 202019 May 2020

Publication series

NameAAMAS Proceedings
ISSN (Electronic)2523-5699

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems
Abbreviated titleAAMAS 2020
CountryNew Zealand
CityVirtual, Auckland
Period19/05/2019/05/20

Bibliographical note

Funding Information:
This work was supported by the Future of Life Institute (grant RFP2-154).

Publisher Copyright:
© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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