Mean-field analysis for cognitively-grounded opinion dynamics with confirmation bias

TL;DR

This paper uses mean-field analysis to study how confirmation bias influences opinion dynamics, revealing how individual biases can lead to social polarization or consensus.

Contribution

It introduces a mean-field approach to a cognitively-grounded opinion model with confirmation bias, providing analytical insights into social polarization phenomena.

Findings

Mean-field predictions align with agent-based simulations.

Confirmation bias induces transitions from consensus to polarization.

The model captures robustness of opinion states under bias.

Abstract

Understanding how individuals' beliefs and attitudes evolve within a population is crucial for explaining social phenomena such as polarization and consensus formation. We explore a persuasive arguments model incorporating confirmation bias, where individuals preferentially accept information aligning with their existing beliefs. By employing a mean-field approach, widely used in statistical physics, we simplify complex processes of argument exchange within the population. Our analysis proceeds by projecting the model onto continuous opinion dynamics and further reducing it through mean-field reasoning. The findings highlight the robustness of mean-field predictions and their compatibility with agent-based simulations, capturing the transition from consensus to polarization induced by confirmation bias.