期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2020
卷号：23
期号：4
页码：1-31
DOI：10.18564/jasss.4379
出版社：University of Surrey, Department of Sociology
摘要：This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local communityresources to meet basic and self-care needs. The diffusion of information about communityresources and their use is modeled via networked interactions and their subsequent effect on agents' use of communityresources across an urban population. A random forest model is iterativelyfitted to model evaluations to characterize the model parameter space with respect to observed empirical data.We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentiallyviable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data.We argue that such methods are necessary to enable model exploration in complexcomputational models that incorporate increasingly available micro-level behavior data.We provide public access to the model and high-performance computing experimentation code.
