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Social Norms of Cooperation With Costly Reputation Building


 
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1. Title Title of document Social Norms of Cooperation With Costly Reputation Building
 
2. Creator Author's name, affiliation, country Fernando P. Santos; INESC-ID and Instituto Superior Técnico, Universidade de Lisboa; Portugal
 
2. Creator Author's name, affiliation, country Jorge M. Pacheco; Centro de Biologia Molecular e Ambiental and Universidade do Minho; Portugal
 
2. Creator Author's name, affiliation, country Francisco C. Santos; INESC-ID and Instituto Superior Técnico, Universidade de Lisboa; Portugal
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Multiagent systems, Social norms, Indirect reciprocity, Cooperation, Reputations, Evolution
 
3. Subject Subject classification Computer Science, Multiagent systems
 
4. Description Abstract Social norms regulate actions in artificial societies, steering collective behavior towards desirable states. In real societies, social norms can solve cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity: reputations of agents are assigned following social norms that identify their actions as good or bad. This, in turn, implies that agents can discriminate between the different actions of others and that the behaviors of each agent are known to the population at large. This is only possible if the agents report their interactions. Reporting constitutes, this way, a fundamental ingredient of indirect reciprocity, as in its absence cooperation in a multiagent system may collapse. Yet, in most studies to date, reporting is assumed to be cost-free, which collides with many life situations, where reporting can easily incur a cost (costly reputation building). Here we develop a new model of indirect reciprocity that allows reputation building to be costly. We show that only two norms can sustain cooperation under costly reputation building, a feature that requires agents to be able to anticipate the reporting intentions of their opponents, depending sensitively on both the cost of reporting and the accuracy level of reporting anticipation.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) This research was supported by FCT-Portugal grants SFRH/BD/86465/2012, SFRH/BD/94736/2013, PTDC/EEI-SII/5081/2014, PTDC/MAT/STA/3358/2014, UID/BIA/04050/2013, and UID/CEC/50021/2013
 
7. Date (YYYY-MM-DD) 2018-04-26
 
8. Type Status & genre Peer-reviewed Paper
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Universal Resource Indicator https://aaai.org:443/ocs/index.php/AAAI/AAAI18/paper/view/16635
 
11. Source Journal/conference title; vol., no. (year) AAAI Conference on Artificial Intelligence; Thirty-Second AAAI Conference on Artificial Intelligence
 
12. Language English=en en
 
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