Norm convergence in populations of dynamically interacting agents

Agent Based Modelling (ABM) is a methodology used to study the behaviour of norms in complex systems. Agent based simulations are capable of generating populations of heterogeneous, self-interested agents that interact with one another. Emergent norm behaviour in the system may then be understood as a result of these individual interactions. Agents observe the behaviour of their group and update their belief based on those of others. Social networks have been shown to play an important role in norm convergence. In this model agents interact on a fixed social network with members of their own social group plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual's path distance on the network. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. Using this method we investigate the effect that random interactions have on the dissemination of social norms when agents are primarily influenced by their social network. We discover that increasing the frequency and quality of random interactions results in an increase in the rate of norm convergence.