Quantitative assessment can stabilize indirect reciprocity under imperfect information

Laura Schmid (), Farbod Ekbatani, Christian Hilbe and Krishnendu Chatterjee
Additional contact information
Laura Schmid: KAIST Graduate School of AI
Farbod Ekbatani: The University of Chicago
Christian Hilbe: Max Planck Institute for Evolutionary Biology
Krishnendu Chatterjee: IST Austria

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract The field of indirect reciprocity investigates how social norms can foster cooperation when individuals continuously monitor and assess each other’s social interactions. By adhering to certain social norms, cooperating individuals can improve their reputation and, in turn, receive benefits from others. Eight social norms, known as the “leading eight," have been shown to effectively promote the evolution of cooperation as long as information is public and reliable. These norms categorize group members as either ’good’ or ’bad’. In this study, we examine a scenario where individuals instead assign nuanced reputation scores to each other, and only cooperate with those whose reputation exceeds a certain threshold. We find both analytically and through simulations that such quantitative assessments are error-correcting, thus facilitating cooperation in situations where information is private and unreliable. Moreover, our results identify four specific norms that are robust to such conditions, and may be relevant for helping to sustain cooperation in natural populations.

Date: 2023
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DOI: 10.1038/s41467-023-37817-x

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