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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., Comment: Keywords: opinion dynamics, validation, empirical confirmation, survey experiments, parameter estimation, argument communication theory, computational social science

In sociological research, the study of macro processes, such as opinion polarization, faces a fundamental problem, the so-called micro-macro problem. To overcome this problem, we combine empirical experimental research on biased argument processing with a computational theory of group deliberation in order to clarify the role of biased processing in debates around energy. The experiment reveals a strong tendency to consider arguments aligned with the current attitude more persuasive and to downgrade those speaking against it. This is integrated into the framework of argument communication theory in which agents exchange arguments about a certain topic and adapt opinions accordingly. We derive a mathematical model that allows to relate the strength of biased processing to expected attitude changes given the specific experimental conditions and find a clear signature of moderate biased processing. We further show that this model fits significantly better to the experimentally observed attitude changes than the neutral argument processing assumption made in previous models. Our approach provides new insight into the relationship between biased processing and opinion polarization. At the individual level our analysis reveals a sharp qualitative transition from attitude moderation to polarization. At the collective level we find (i.) that weak biased processing significantly accelerates group decision processes whereas (ii.) strong biased processing leads to a persistent conflictual state of subgroup polarization. While this shows that biased processing alone is sufficient for the emergence of polarization, we also demonstrate that homophily may lead to intra-group conflict at significantly lower rates of biased processing.