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Agent-Based Model Calibration using Machine Learning Surrogates

Francesco Lamperti (), Andrea Roventini () and Amir Sani ()
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Amir Sani: CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the " Island " endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large out-of-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces.

Keywords: meta-model; agent based model; surrogate; calibration; machine learning (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
Date: 2017-04-03
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01499344
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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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