2017/11 | LEM Working Paper Series | ||||||||||||||||
Agent-Based Model Calibration using Machine Learning Surrogates |
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Francesco Lamperti, Andrea Roventini and Amir Sani |
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Keywords | |||||||||||||||||
agent based model; calibration; machine learning; surrogate; meta-model
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JEL Classifications | |||||||||||||||||
C15, C52, C63
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Abstract | |||||||||||||||||
Efficiently calibrating agent-based models (ABMs) to real data is an
open challenge. This paper explicitly tackles parameter space
exploration and calibration of ABMs by combining machine-learning and
intelligent iterative sampling. The proposed approach "learns" a fast
surrogate meta-model using a limited number of ABM evaluations and
approximates the nonlinear relationship between ABM inputs (initial
conditions and parameters) and outputs. Performance is evaluated on
the (Brock and Hommes, 1998) asset pricing model and the ``Islands''
endogenous growth model (Fagiolo and Dosi, 2003). Results demonstrate
that machine learning surrogates obtained using the proposed iterative
learning procedure provide a quite accurate proxy of the true model
and dramatically reduce the computation time necessary for large scale
parameter space exploration and calibration.
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