Validating argument-based opinion dynamics with survey experiments

• URL: http://arxiv.org/abs/2212.10143v2
• Date: Fri, 21 Jul 2023 13:43:36 GMT
• Title: Validating argument-based opinion dynamics with survey experiments
• Authors: Sven Banisch and Hawal Shamon
• Abstract summary: The empirical validation of models remains one of the most important challenges in opinion dynamics. We 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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