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.

翻译：意见动态模型中，实证验证仍是其面临的最重要挑战之一。本文报告了将调查实验数据与意见形成计算模型相结合的最新进展，并扩展了此前针对以偏见处理为主要机制的论证型意见动态模型的实证评估工作。此前研究（Banisch & Shamon，即将发表）聚焦于利用论证诱导意见变化的实验数据校准微观机制，而本文则基于调查实验收集的实证数据，重点考察宏观层面。为此，我们通过引入平衡信息的外部来源扩展了论证模型，从而能够控制同伴影响过程相对于其他噪声过程的影响力。研究表明，在参数空间的特定区域内，调查所得的意见分布能够以高精度匹配，这表明社会影响与外部噪声的影响程度相当。更重要的是，基于宏观数据估算的偏见处理强度与微观层面实现高似然性的参数值兼容。因此，本文的主要贡献在于证明扩展后的论证模型为从论证诱导态度变化的微观过程到宏观意见分布之间建立了坚实桥梁。此外，我们回顾了论证型模型的发展历程，并提出了一种自动化分类模型结果的新方法。