Bounded confidence opinion dynamics with Asch-like social conformity in complex networks

Abstract

Computational models of peer interaction, with or without networks, have been applied to opinion dynamics to describe social phenomena. Here, we use the Deffuant–Weisbuch (DW) model of opinion dynamics, where a confidence parameter bounds individuals’ interactions, both in paradigmatic artificial networks and some social networks. The interaction of an individual with their immediate neighbors is incorporated into the model using Asch’s concept of social conformity. In general, conformity facilitates consensus in networks by reducing the time required to reach a state of equilibrium and by increasing the likelihood of a single opinion value prevailing throughout the network. In real networks, a higher probability of adherence (\(p_{Asch}=0.6\)) to the majority opinion increases the proportion of individuals in consensus within less tolerant networks (\(d<0.5\)). Conformity leads to more nodes agreeing around the same average opinion in a shorter time.