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Pré-Publication, Document De Travail Année : 2017

Agent-Based Model Calibration using Machine Learning Surrogates

Résumé

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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Dates et versions

hal-01499344 , version 1 (03-04-2017)

Identifiants

  • HAL Id : hal-01499344 , version 1

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Francesco Lamperti, Andrea Roventini, Amir Sani. Agent-Based Model Calibration using Machine Learning Surrogates. 2017. ⟨hal-01499344⟩
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