The observation of tolerance in a social network model

Abstract

In dynamic social networks, agents are able to make and break connections with neighbors to improve their payoffs. Rules have recently developed which help agents evaluate their neighbors and decide whether to break a connection. These rules have introduced the idea of tolerance in dynamic networks by allowing an agent to maintain a relationship with a bad neighbor for some time. In this research, we investigate and define the phenomenon of tolerance in dynamic social networks, particularly with the Highest Weighted Reward rule. We define a mathematical model to predict an agent's tolerance of a bad neighbor and determine the factors that affect it. Tolerance of other agents is an intuitive human behavior, and its presence in social network models suggests that the development of social interaction rules among agents shows qualities of human interactions that exist in real life.