英文标题：
《Agent-Based Model Calibration using Machine Learning Surrogates》
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作者：
Francesco Lamperti, Andrea Roventini and Amir Sani
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最新提交年份：
2017
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英文摘要：
  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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中文摘要：
让基于代理的模型（ABM）更接近数据是一个公开的挑战。本文结合有监督机器学习和智能采样，明确地解决了ABMs的参数空间探索和校准问题，建立了一个代理元模型。所提出的方法提供了模型行为的快速准确近似，大大减少了计算时间。在这方面，我们的机器学习代理有助于大规模探索参数空间，同时提供了一个强大的过滤器来深入了解基于代理的模型的复杂功能。本文介绍的算法将模型仿真和输出分析合并到代理元模型中，大大简化了ABM校准。我们成功地将我们的方法应用于Brock和Hommes（1998）资产定价模型和“岛屿”内生增长模型（Fagiolo和Dosi，2003）。针对相对较大的样本外参数组合对性能进行评估，同时采用不同的用户定义统计测试进行输出分析。结果表明，机器学习代理能够在其通常崎岖的参数空间上快速而精确地探索基于代理的模型的行为。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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