Why Groups Show Different Fairness Norms? The Interaction Topology Might Explain
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Computational models of prosocial norms are becoming important from the perspective of theoretical social sciences as well as engineering of autonomous systems, who also need to show prosocial behavior in their social interactions. Fairness, as one of the strongest prosocial norms has long been argued to govern human behavior in a wide range of social, economic, and organizational activities. The sense of fairness, although universal, varies across different societies. In this study, using a computational model based on evolutionary games on graphs, we demonstrate emergence of fair behavior in structured interaction of rational agents and test the hypothesis that the network structure of social interaction can causally explain some of the cross-societal variations in fairness norms, as previously reported by empirical studies. We show that two network parameters, community structure, as measured by the modularity index, and network hubiness, represented by the skewness of degree distribution, have the most significant impact on emergence of fairness norms. These two parameters can explain much of the variations in fairness norms across societies and can also be linked to hypotheses suggested by earlier empirical work. We devised a multi-layered model that combines local agent interactions with social learning, thus enables both strategic behavior as well as diffusion of successful strategies. We also discuss some generalizable methods that are used in the selection of network structures and convergence criteria used in simulations for work. By applying multivariate statistics on the results, we obtain the relation between network structural features and the collective fair behavior.
KeywordsSocial norms Fairness Social network Ultimatum game
This work is in part supported by the National Science Foundation (NSF) under Grant No. CMMI-1548521. Authors are grateful to David Gianetto for the insightful comments on the computational framework.
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