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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.
|Title of host publication||Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020|
|Editors||Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||3|
|Publication status||Published - 9 May 2020|
|Event||19th International Conference on Autonomous Agents and Multiagent Systems - Virtual, Auckland, New Zealand|
Duration: 19 May 2020 → 19 May 2020
|Conference||19th International Conference on Autonomous Agents and Multiagent Systems|
|Abbreviated title||AAMAS 2020|
|Period||19/05/20 → 19/05/20|
Bibliographical noteFunding Information:
This work was supported by the Future of Life Institute (grant RFP2-154).
© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.
Copyright 2020 Elsevier B.V., All rights reserved.
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