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Agent-Based Model Calibration using Machine Learning Surrogates

Listed author(s):
  • Francesco Lamperti

    ()

    (Laboratory of Economics and Management (LEM) - Scuola Superiore Sant'Anna [Pisa])

  • Andrea Roventini

    ()

    (OFCE - Observatoire Français des Conjonctures économiques - Institut d'Études Politiques [IEP] - Paris - Fondation Nationale des Sciences Politiques [FNSP], Laboratory of Economics and Management (LEM) - Scuola Superiore Sant'Anna [Pisa])

  • Amir Sani

    ()

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

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.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number hal-01499344.

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Date of creation: 03 Apr 2017
Handle: RePEc:hal:cesptp:hal-01499344
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