Validating argument-based opinion dynamics with survey experiments

TL;DR

This paper combines survey experiment data with an extended argument-based opinion dynamics model to validate how micro-level argument processing influences macro-level opinion distributions, highlighting the roles of social influence and external noise.

Contribution

It extends previous models by incorporating external balanced information and demonstrates the model's ability to accurately reproduce survey-based opinion distributions at the macro level.

Findings

Survey data matches model predictions with high accuracy.

Equal impact of social influence and external noise on opinions.

Estimated biased processing strength aligns with micro-level likelihoods.

Abstract

The empirical validation of models remains one of the most important challenges in opinion dynamics. In this contribution, we report on recent developments on combining data from survey experiments with computational models of opinion formation. We extend previous work on the empirical assessment of an argument-based model for opinion dynamics in which biased processing is the principle mechanism. While previous work (Banisch & Shamon, in press) has focused on calibrating the micro mechanism with experimental data on argument-induced opinion change, this paper concentrates on the macro level using the empirical data gathered in the survey experiment. For this purpose, the argument model is extended by an external source of balanced information which allows to control for the impact of peer influence processes relative to other noisy processes. We show that surveyed opinion distributions are matched with a high level of accuracy in a specific region in the parameter space, indicating an equal impact of social influence and external noise. More importantly, the estimated strength of biased processing given the macro data is compatible with those values that achieve high likelihood at the micro level. The main contribution of the paper is hence to show that the extended argument-based model provides a solid bridge from the micro processes of argument-induced attitude change to macro level opinion distributions. Beyond that, we review the development of argument-based models and present a new method for the automated classification of model outcomes.