Opinion Dynamics with Highly Oscillating Opinions

TL;DR

This paper investigates the ability of various Opinion Dynamics models to replicate highly oscillating opinions, using an optimization approach and real-world data, highlighting the ATBCR model's superior performance.

Contribution

It introduces an optimization framework to evaluate OD models' capacity to simulate oscillating opinions and identifies the ATBCR model as most effective for real-world data.

Findings

ATBCR model best captures oscillating opinions in data.

Optimization approach effectively assesses OD models.

Real-world dataset validates model performance.

Abstract

Opinion Dynamics (OD) models are a particular case of Agent-Based Models in which the evolution of opinions within a population is studied. In most OD models, opinions evolve as a consequence of interactions between agents, and the opinion fusion rule defines how those opinions are updated. In consequence, despite being simplistic, OD models provide an explainable and interpretable mechanism for understanding the underlying dynamics of opinion evolution. Unfortunately, existing OD models mainly focus on explaining the evolution of (usually synthetic) opinions towards consensus, fragmentation, or polarization, but they usually fail to analyze scenarios of (real-world) highly oscillating opinions. This work overcomes this limitation by studying the ability of several OD models to reproduce highly oscillating dynamics. To this end, we formulate an optimization problem which is further solved using Evolutionary Algorithms, providing both quantitative results on the performance of the optimization and qualitative interpretations on the obtained results. Our experiments on a real-world opinion dataset about immigration from the monthly barometer of the Spanish Sociological Research Center show that the ATBCR, based on both rational and emotional mechanisms of opinion update, is the most accurate OD model for capturing highly oscillating opinions.