Agent-Based Model Calibration using Machine Learning Surrogates

Francesco Lamperti (), Andrea Roventini and Amir Sani ()
Additional contact information
Francesco Lamperti: Laboratory of Economics and Management (LEM) - SSSUP - Scuola Universitaria Superiore Sant'Anna = Sant'Anna School of Advanced Studies [Pisa]
Amir Sani: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

SciencePo Working papers Main 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: surrogate; calibration; machine learning; agent based model; meta-model (search for similar items in EconPapers)
Date: 2017-04-03
Note: View the original document on HAL open archive server: https://hal.science/hal-01499344
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://hal.science/hal-01499344/document (application/pdf)

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)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:spmain:hal-01499344

Access Statistics for this paper

More papers in SciencePo Working papers Main from HAL
Bibliographic data for series maintained by Contact - Sciences Po Departement of Economics ().