Clustering in co-evolving opinion dynamics: reduced SPDE models

TL;DR

This paper introduces reduced stochastic PDE models to efficiently simulate clustering phenomena in co-evolving opinion dynamics, capturing long-term behaviour with lower computational cost.

Contribution

It extends reduced PDE modeling to a stochastic framework for opinion dynamics, enabling efficient long-term clustering analysis in large populations.

Findings

Reduced SPDE models accurately reproduce clustering behaviour.

Proposed models significantly decrease computational cost.

Application to large-scale social survey data demonstrates practical utility.

Abstract

Clustering is a fundamental collective phenomenon in agent-based models (ABMs) of opinion dynamics. To study clustering in systems with co-evolving social and opinion variables, we derive stochastic partial differential equation (SPDE) models that describe the evolution of clusters on a reduced state space. We consider two settings: one in which opinions do not affect social interactions, and another one in which a feedback mechanism couples the two. Our approach extends reduced PDE modelling to a stochastic framework, which is essential for capturing long-term cluster behaviour. Numerical experiments demonstrate that the proposed reduced SPDEs substantially decrease computational cost compared to full-state SPDE models, such as the Dean-Kawasaki equation, while still accurately reproducing the clustering behaviour of the underlying ABM. As a result, these reduced models provide an efficient tool for studying systems with large populations, including those arising in the analysis of real-world data: in particular, we provide an application related to the large-scale General Social Survey (GSS), which comprises opinion and social data of the US population since 1972.