Classification-Based Opinion Formation Model Embedding Agents' Psychological Traits

TL;DR

This paper introduces an agent-based opinion formation model that incorporates realistic classification of opinions and psychological traits, capable of predicting complex social phenomena like polarization and consensus.

Contribution

The model uniquely combines classification-based opinion updates with psychological traits, enhancing realism and predictive power in simulating opinion dynamics.

Findings

Model reproduces polarization, consensus, and clustering behaviors.

Predicted opinions align with real survey data.

Incorporates psychological traits for richer social dynamics.

Abstract

We propose an agent-based opinion formation model characterised by a two-fold novelty. First, we realistically assume that each agent cannot measure the opinion of its neighbours with infinite resolution and accuracy, and hence it can only classify the opinion of others as agreeing much more, or more, or comparably, or less, or much less (than itself) with a given statement. This leads to a classification-based rule for opinion update. Second, we consider three complementary agent traits suggested by significant sociological and psychological research: conformism, radicalism and stubbornness. We rely on World Values Survey data to show that the proposed model has the potential to predict the evolution of opinions in real life: the classification-based approach and complementary agent traits produce rich collective behaviours, such as polarisation, consensus, and clustering, which can yield predicted opinions similar to survey results.