Agent-Based Model Calibration using Machine Learning Surrogates

Francesco Lamperti, Andrea Roventini and Amir Sani
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Francesco Lamperti: UP1 - Université Paris 1 Panthéon-Sorbonne
Amir Sani: UP1 - Université Paris 1 Panthéon-Sorbonne

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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 outof-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: Agent-based model; Calibration; Machine learning; Surrogate; Meta-model (search for similar items in EconPapers)
Date: 2017-03-01
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03458875
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Citations: View citations in EconPapers (15)

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Related works:
Journal Article: Agent-based model calibration using machine learning surrogates (2018)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-based model calibration using machine learning surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
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