Long-Time and Short-Time Dynamics in a Weighted-Median Opinion Model on Networks

TL;DR

This paper investigates a weighted-median opinion model on networks, analyzing how network structure influences long-term and short-term opinion dynamics through simulations and mean-field approximations.

Contribution

It introduces a median-based opinion update model and derives a mean-field approximation for large networks, providing insights into opinion formation processes.

Findings

Network structure significantly affects opinion consensus and fragmentation.

The mean-field approximation accurately predicts short-time dynamics.

Median-based updates lead to different opinion patterns compared to mean-based models.

Abstract

Social interactions influence people's opinions. In some situations, these interactions eventually yield a consensus opinion; in others, they can lead to opinion fragmentation and the formation of different opinion groups in the form of ``echo chambers''. Consider a social network of individuals with continuous-valued scalar opinions, and suppose that they can change their opinions when they interact with each other. In many models of the opinion dynamics of individuals in a network, it is common for opinion updates to depend on the mean opinion of interacting individuals. As an alternative, which may be more realistic in some situations, we study an opinion model with an opinion-update rule that depends on the weighted median of the opinions of interacting individuals. Through numerical simulations of our median-update opinion model, we investigate how the final opinion distribution depends on network structure. For configuration-model networks, we also derive a mean-field approximation for the asymptotic dynamics of the opinion distribution when there are infinitely many individuals. We numerically investigate its accuracy for short-time opinion dynamics on various networks.