Effects of heterogeneous convergence rate on consensus in opinion dynamics

Abstract

The Deffuant model has attracted much attention in the study of opinion dynamics. Here, we propose a modified version by introducing into the model a heterogeneous convergence rate which is dependent on the opinion difference between interacting agents and a tunable parameter κ. We study the effects of heterogeneous convergence rate on consensus by investigating the probability of complete consensus, the size of the largest opinion cluster, the number of opinion clusters, and the relaxation time. We find that the decrease of the convergence rate is favorable to decreasing the confidence threshold for the population to always reach complete consensus, and there exists optimal κ resulting in the minimal bounded confidence threshold. Moreover, we find that there exists a window before the threshold of confidence in which complete consensus may be reached with a nonzero probability when κ is not too large. We also find that, within a certain confidence range, decreasing the convergence rate will reduce the relaxation time, which is somewhat counterintuitive.