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.

本文提出了一种新的方法，将监督机器学习和智能抽样结合起来，构建一个代理元模型来探索代理模型中的参数空间和校准。这种方法提供了一种快速而准确的模型行为近似方法，大大降低了计算时间，并有助于获取有关代理模型复杂运作的洞见。