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The Co-Evolution of Fairness Preferences and Costly Punishment

We study the co-evolutionary emergence of fairness preferences in the form of other-regarding behavior and its effect on the origination of costly punishment behavior in public good games. Our approach closely combines empirical results from three experiments with an evolutionary simulation model. I...

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Detalles Bibliográficos
Autores principales: Hetzer, Moritz, Sornette, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3603958/
https://www.ncbi.nlm.nih.gov/pubmed/23526926
http://dx.doi.org/10.1371/journal.pone.0054308
Descripción
Sumario:We study the co-evolutionary emergence of fairness preferences in the form of other-regarding behavior and its effect on the origination of costly punishment behavior in public good games. Our approach closely combines empirical results from three experiments with an evolutionary simulation model. In this way, we try to fill a gap between the evolutionary theoretical literature on cooperation and punishment on the one hand and the empirical findings from experimental economics on the other hand. As a principal result, we show that the evolution among interacting agents inevitably favors a sense for fairness in the form of “disadvantageous inequity aversion”. The evolutionary dominance and stability of disadvantageous inequity aversion is demonstrated by enabling agents to co-evolve with different self- and other-regarding preferences in a competitive environment with limited resources. Disadvantageous inequity aversion leads to the emergence of costly (“altruistic”) punishment behavior and quantitatively explains the level of punishment observed in contemporary lab experiments performed on subjects with a western culture. Our findings corroborate, complement, and interlink the experimental and theoretical literature that has shown the importance of other-regarding behavior in various decision settings.