Surrogate Modelling in (and of) Agent-Based Models: A Prospectus

被引:21
|
作者
van der Hoog, Sander [1 ]
机构
[1] Bielefeld Univ, Dept Business Adm & Econ, Econ Theory & Computat Econ ETACE, Bielefeld, Germany
基金
欧盟地平线“2020”;
关键词
Surrogate modelling; Agent-based models; Estimation; ECONOMIC-POLICY; NEURAL-NETWORK;
D O I
10.1007/s10614-018-9802-0
中图分类号
F [经济];
学科分类号
02 ;
摘要
A very timely issue for economic agent-based models (ABMs) is their empirical estimation. This paper describes a line of research that could resolve the issue by using machine learning techniques, using multi-layer artificial neural networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et al. (Neural Comput 18(7):1527-1554, 2006) introduced a fast and efficient training algorithm called Deep Learning, and there have been major breakthroughs in machine learning ever since. Economics has not yet benefited from these developments, and therefore we believe that now is the right time to apply multi-layered ANNs and Deep Learning to ABMs in economics.
引用
收藏
页码:1245 / 1263
页数:19
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