文章基本信息

标题：Calibrating Agent-Based Models with Linear Regressions
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作者：Ernesto Carrella ; Richard Bailey ; Jens Madsen 等
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：1
页码：1-23
DOI：10.18564/jasss.4150
出版社：University of Surrey, Department of Sociology
摘要：In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification, we can extend this approach to model selection. We present five example estimations: a statistical fit, a biological individual-based model, a simple real business cycle model, a non-linear biological simulation and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and uses them to parametrize models without running any direct minimization.
关键词：Agent-Based Models; Indirect Inference; Estimation; Calibration; Simulated Minimum Distance; Approximate Bayesian Computation