期刊名称:Journal of Artificial Societies and Social Simulation
印刷版ISSN:1460-7425
出版年度:2021
卷号:24
期号:2
页码:1
DOI:10.18564/jasss.4573
出版社:University of Surrey, Department of Sociology
摘要:Computational social science has witnessed a shi from pure theoretical to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents’ decisions. There is a strong urge to go beyond theoretical ABMs with behavioral theories setting stylized rules that guide agents’ actions, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, a choice of a policy when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environmental policies that increasingly rely on quantifying cumulative eects of individual behavioral changes, where ABMs are so helpful. We propose a comparison framework to address this methodological dilemma, which quantitatively explores the gap in predictions between theory- and data-driven ABMs. Inspired by the existing theory-driven model, ORVin-T, which studies the individual choice between organic and conventional products, we design a survey to collect data on individual preferences and purchasing decisions. We then use this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents’ decision strategies with our empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis, and explore three policy scenarios. We observe that the theory-driven model predicts the shis to organic consumption as accurately as the ABM with empirical micro-foundations at both aggregated and individual scales. There are slight dierences (±5%) between the estimations of the two models with regard to dierent behavioral change scenarios: increasing conventional tax, launching organic social-informational campaigns, and their combination. Our findings highlight the goodness of fit and usefulness of theoretical modeling eorts, at least in the case of incremental behavioral change. It sheds light on the conditions when theory-driven and data-driven models are aligned and on the value of empirical data for studying systemic changes.