Emergence of Social Norms in Complex Networks

Abstract

This paper studies the problem that how social norms emerge even though agents are selfish and attempt to only maximize their own utility. We propose a new rule for social interactions. The rule is called Highest Rewarding Neighborhood (HRN). The HRN rule allows agents to remain selfish and be able to break relationships in order to maximize their utility. Our experiment shows that when agents are able to break unrewarding relationships that a Pareto-optimum strategy arises as the social normal. In addition we conclude the rate and amount of Pareto-optimum strategy that arises is dependent on the network structure when the networks are dynamic, and the rate is independent of the network structure when the networks are static.