Calibrating agent-based models with linear regressions

Journal article

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.

Authors: Carrella, E; Bailey, R; Madsen, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SimSoc Consortium
• Journal: Journal of Artificial Societies and Social Simulation
• Volume: 23
• Issue: 1
• Pages: 1-7
• Publication date: 2020-01-31
• Acceptance date: 2019-11-25
• DOI: 10.18564/jasss.4150
• EISSN: 1460-7425
• ISSN: 1460-7425
• Keywords: estimation; agent-based models; calibration; indirect inference; approximate Bayesian Computation; simulated minimum distance; FFR