Opinion Dynamics with Highly Oscillating Opinions

2025-06-25

AI Summary

Existing opinion dynamics (OD) models predominantly capture consensus formation or polarization, failing to reproduce the high-frequency oscillations frequently observed in real-world opinion evolution. Method: This paper presents the first systematic evaluation of mainstream OD models’ capacity to replicate oscillatory opinion trajectories and introduces the ATBCR model—a novel framework integrating rational deliberation and affective influence mechanisms. Parameter optimization is performed via evolutionary algorithms, and model performance is rigorously assessed using both quantitative metrics and qualitative analysis on empirical survey data regarding public attitudes toward immigration in Spain. Contribution/Results: ATBCR significantly outperforms benchmark models, accurately capturing rapid opinion fluctuations and non-monotonic evolutionary patterns. It offers a theoretically grounded, interpretable modeling paradigm for complex opinion dynamics, advancing both analytical capability and mechanistic understanding of real-world sociopolitical discourse.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Analyzing highly oscillating opinions in Opinion Dynamics models
• Evaluating OD models' ability to reproduce opinion oscillations
• Optimizing OD models for real-world oscillating opinion data

Innovation

• Evolutionary Algorithms optimize opinion dynamics models
• ATBCR model combines rational and emotional updates
• Real-world dataset validates oscillating opinion analysis