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Agent-Based Model Calibration using Machine Learning Surrogates

Francesco Lamperti (), Andrea Roventini () and Amir Sani

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: Efficiently calibrating agent-based models (ABMs) to real data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs by combining machine-learning and intelligent iterative sampling. The proposed approach "learns" a fast surrogate meta-model using a limited number of ABM evaluations and approximates the nonlinear relationship between ABM inputs (initial conditions and parameters) and outputs. Performance is evaluated on the (Brock and Hommes, 1998) asset pricing model and the ``Islands'' endogenous growth model (Fagiolo and Dosi, 2003). Results demonstrate that machine learning surrogates obtained using the proposed iterative learning procedure provide a quite accurate proxy of the true model and dramatically reduce the computation time necessary for large scale parameter space exploration and calibration.

Keywords: agent based model; calibration; machine learning; surrogate; meta-model (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-hme
Date: 2017-03-04
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Related works:
Journal Article: Agent-based model calibration using machine learning surrogates (2018) Downloads
Working Paper: Agent based model calibration using machine learning surrogates (2018) Downloads
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017) Downloads
Working Paper: Agent-based model calibration using machine learning surrogates (2017) Downloads
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017) Downloads
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017) Downloads
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