Limiting behavior for a general class of voter models with confidence threshold

TL;DR

This paper studies a generalized voter model with a confidence threshold on opinion differences, analyzing conditions for consensus, fixation, and fluctuation across various graph structures.

Contribution

It introduces a new class of opinion dynamics models incorporating confidence thresholds and provides universal bounds and conditions for their long-term behavior.

Findings

Fluctuation and clustering occur when confidence threshold exceeds graph radius.

Universal lower bound for consensus probability on finite graphs.

Sufficient conditions for fixation based on opinion graph structure.

Abstract

This article is concerned with a general class of stochastic spatial models for the dynamics of opinions. Like in the voter model, individuals are located on the vertex set of a connected graph and update their opinion at a constant rate based on the opinion of their neighbors. However, unlike in the voter model, the set of opinions is represented by the set of vertices of another connected graph that we call the opinion graph: when an individual interacts with a neighbor, she imitates this neighbor if and only if the distance between their opinions, defined as the graph distance induced by the opinion graph, does not exceed a certain confidence threshold. When the confidence threshold is at least equal to the radius of the opinion graph, we prove that the one-dimensional process fluctuates and clusters and give a universal lower bound for the probability of consensus of the process on finite connected graphs. We also establish a general sufficient condition for fixation of the infinite system based on the structure of the opinion graph, which we then significantly improve for opinion graphs which are distance-regular. Our general results are used to understand the dynamics of the system for various examples of opinion graphs: paths and stars, which are not distance-regular, and cycles, hypercubes and the five Platonic solids, which are distance-regular.