Bounded-confidence models of opinion dynamics with neighborhood effects.

Abstract

People's opinions evolve through social interactions, and their social networks evolve as their opinions change. In this paper, we generalize bounded-confidence models of opinion dynamics by incorporating neighborhood effects. In our neighborhood bounded-confidence models (NBCMs), interacting agents are influenced both by each other's opinions and by the opinions of the agents in each other's neighborhoods (through so-called "transitive influence"). We extend our NBCMs to adaptive network models that incorporate "transitive homophily", with agents rewiring to connect preferentially to agents whose mean neighbor opinions are close to their own opinions. We numerically simulate an asynchronously updating adaptive NBCM on a variety of networks, and we explore how its opinion dynamics and structural properties change as we adjust the relative importances of dyadic and transitive influence. We observe that the transitive influence of neighborhoods can play an important role in shaping the qualitative features of opinion dynamics.