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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Indirect Reciprocity and Costly Assessment in Multiagent Systems

March 15, 2023

Authors

Fernando Santos

INESC-ID and Instituto Superior Técnico


Jorge Pacheco

Centro de Biologia Molecular e Ambiental and Universidade do Minho


Francisco Santos

INESC-ID and Instituto Superior Técnico


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

Student Abstract Track

Downloads:

Download PDF

Abstract:

Social norms can help solving cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity (IR). Under IR, agents are associated with different reputations, whose attribution depends on socially adopted norms that judge behaviors as good or bad. While the pros and cons of having a certain public image depend on how agents learn to discriminate between reputations, the mechanisms incentivizing agents to report the outcome of their interactions remain unclear, especially when reporting involves a cost (costly reputation building). Here we develop a new model---inspired in evolutionary game theory---and show that two social norms can sustain high levels of cooperation, even if reputation building is costly. For that, agents must be able to anticipate the reporting intentions of their opponents. Cooperation depends sensitively on both the cost of reporting and the accuracy level of reporting anticipation.

DOI:

10.1609/aaai.v32i1.12148


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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