期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2022
卷号：25
期号：3
DOI：10.18564/jasss.4868
语种：English
出版社：University of Surrey, Department of Sociology
摘要：Modeling human behavior in the context of social systems in which we are embedded realistically requires capturing the underlying heterogeneity in human populations. However, trade-offs associated with different approaches to introducing heterogeneity could either enhance or obfuscate our understanding of outcomes and the processes by which they are generated. Thus, the question arises: how to incorporate heterogeneity when modeling human behavior as part of population-scale phenomena such that greater understanding is obtained? We use an agent-based model to compare techniques of introducing heterogeneity at initialization or generated during the model’s runtime. We show that initializations with unstructured heterogeneity can interfere with a structural understanding of emergent processes, especially when structural heterogeneity might be a key part of driving how behavioral responses dynamically shape emergence in the system. We find that incorporating empirical population heterogeneity – even in a limited sense – can substantially contribute to improved understanding of how the system under study works.
关键词：Agent-Based Model;Diffusion Model;Empirical Data-Driven Model; Heterogeneous Population; Model Performance;COVID-19
