Vector opinion dynamics in a model for social influence

TL;DR

This paper introduces a binary vector opinion model for social influence, analyzing how opinions evolve and form clusters or consensus under different network structures and thresholds.

Contribution

It presents a novel binary vector opinion model and compares opinion dynamics on complete and small-world networks, highlighting the impact of thresholds and convergence parameters.

Findings

Steady states depend on threshold and convergence parameter

Transition from clustered to homogeneous opinions analyzed

Differences between complete and small-world networks explored

Abstract

We present numerical simulations of a model of social influence, where the opinion of each agent is represented by a binary vector. Agents adjust their opinions as a result of random encounters, whenever the difference between opinions is below a given threshold. Evolution leads to a steady state, which highly depends on the threshold and a convergence parameter of the model. We analyze the transition between clustered and homogeneous steady states. Results of the cases of complete mixing and small-world networks are compared.