A Utility-Driven Bounded-Confidence Model for Opinion Dynamics

TL;DR

This paper presents a utility-driven bounded-confidence model for opinion dynamics, deriving a stochastic differential equation that captures opinion evolution, metastability, and cluster merging, aligning well with agent-based simulations.

Contribution

It introduces a novel utility-driven bounded-confidence model and derives a stochastic differential equation to describe opinion dynamics, including metastability and cluster behavior.

Findings

Stationary distribution is Gibbs-like with an effective potential.

Dynamics show metastability and spontaneous switching between opinions.

Reduced model accurately predicts opinion cluster evolution.

Abstract

We introduce a utility-driven bounded-confidence model of opinion dynamics in which opinions associated with higher utility exert stronger social influence. In the regime where all agents belong to a single opinion cluster, we derive a stochastic differential equation for the mean opinion and show that its stationary distribution is Gibbs-like, with an effective potential determined by the utility landscape and an inverse temperature controlled by the learning rate and the number of agents. For multimodal utility functions, the dynamics exhibit metastability and spontaneous switching between competing opinion states. The reduced stochastic description also captures the evolution and merging of multiple opinion clusters, in agreement with agent-based simulations.