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  Evolution of cooperation through cumulative reciprocity

Li, J., Zhao, X., Li, B., Rossetti, C. S. L., Hilbe, C., & Xia, H. (2022). Evolution of cooperation through cumulative reciprocity. Nature Computational Science, 2, 677-686. doi:10.1038/s43588-022-00334-w.

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 Urheber:
Li, Juan, Autor
Zhao, Xiaowei, Autor
Li, Bing, Autor
Rossetti, Charlotte S. L.1, 2, Autor           
Hilbe, Christian2, Autor           
Xia, Haoxiang, Autor
Affiliations:
1IMPRS for Evolutionary Biology, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445639              
2Max Planck Research Group Dynamics of Social Behavior (Hilbe), Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_3164873              

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 Zusammenfassung: Reciprocity is a simple principle for cooperation that explains many of the patterns of how humans seek and receive help from each other. To capture reciprocity, traditional models often assume that individuals use simple strategies with restricted memory. These memory-1 strategies are mathematically convenient, but they miss important aspects of human reciprocity, where defections can have lasting effects. Here we instead propose a strategy of cumulative reciprocity. Cumulative reciprocators count the imbalance of cooperation across their previous interactions with their opponent. They cooperate as long as this imbalance is sufficiently small. Using analytical and computational methods, we show that this strategy can sustain cooperation in the presence of errors, that it enforces fair outcomes and that it evolves in hostile environments. Using an economic experiment, we confirm that cumulative reciprocity is more predictive of human behaviour than several classical strategies. The basic principle of cumulative reciprocity is versatile and can be extended to a range of social dilemmas.

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Sprache(n): eng - English
 Datum: 2021-10-062022-09-142022-10-20
 Publikationsstatus: Online veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s43588-022-00334-w
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Titel: Nature Computational Science
  Kurztitel : Nat Comput Sci
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: London, UK : Nature Research
Seiten: - Band / Heft: 2 Artikelnummer: - Start- / Endseite: 677 - 686 Identifikator: ISSN: 2662-8457
CoNE: https://pure.mpg.de/cone/journals/resource/2662-8457