The Affective Evolution of Social Norms in Social Networks

Social norms are a core concept in social sciences and play a critical role in regulating a society's behavior. Organizations and even governmental bodies use this social component to tackle varying challenges in the society, as it is a less costly alternative to establishing new laws and regulations. Social networks are an important and effective infrastructure in which social norms can evolve. Therefore, there is a need for theoretical models for studying the spread of social norms in social networks. In this paper, by using the intrinsic properties of norms, we redefine and tune the Rescorla-Wagner conditioning model in order to obtain an effective model for the spread of social norms. We extend this model for a network of people as a Markov chain. The potential structures of steady states in this process are studied. Then, we formulate the problem of maximizing the adoption of social norms in a social network by finding the best set of initial norm adopters. Finally, we propose an algorithm for solving this problem that runs in polynomial time and experiments it on different networks. Our experiments show that our algorithm has superior performance over other methods.