Statistics of opinion domains of the majority-vote model on a square lattice

TL;DR

This paper investigates the formation and distribution of opinion domains in a majority-vote social influence model on a square lattice, revealing complex domain structures, their statistical properties, and effects of noise through simulations and analytical approximations.

Contribution

It introduces a variant of the majority-vote model with a tie-retention rule, analyzes domain statistics using mean-field and pair approximations, and explores the impact of noise on opinion stability.

Findings

Number of opinion domains grows with lattice size squared

Largest domain size scales logarithmically with lattice size

Opinion domain sizes follow a power-law distribution for small sizes

Abstract

The existence of juxtaposed regions of distinct cultures in spite of the fact that people's beliefs have a tendency to become more similar to each other's as the individuals interact repeatedly is a puzzling phenomenon in the social sciences. Here we study an extreme version of the frequency-dependent bias model of social influence in which an individual adopts the opinion shared by the majority of the members of its extended neighborhood, which includes the individual itself. This is a variant of the majority-vote model in which the individual retains its opinion in case there is a tie among the neighbors' opinions. We assume that the individuals are fixed in the sites of a square lattice of linear size $L$ and that they interact with their nearest neighbors only. Within a mean-field framework, we derive the equations of motion for the density of individuals adopting a particular opinion in the single-site and pair approximations. Although the single-site approximation predicts a single opinion domain that takes over the entire lattice, the pair approximation yields a qualitatively correct picture with the coexistence of different opinion domains and a strong dependence on the initial conditions. Extensive Monte Carlo simulations indicate the existence of a rich distribution of opinion domains or clusters, the number of which grows with $L^2$ whereas the size of the largest cluster grows with $\ln L^2$. The analysis of the sizes of the opinion domains shows that they obey a power-law distribution for not too large sizes but that they are exponentially distributed in the limit of very large clusters. In addition, similarly to other well-known social influence model -- Axelrod's model -- we found that these opinion domains are unstable to the effect of a thermal-like noise.