Surrogate Modelling in (and of) Agent-Based Models: A Prospectus
Surrogate Modelling in (and of) Agent-Based Models: A Prospectus
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基于代理的模型中的代理建模：说明书
DOI：
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发表时间：
2018
影响因子：
2
通讯作者：
Sander van der Hoog
中科院分区：
文献类型：
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作者：
Sander van der Hoog
基于经济主体的模型(ABM)的一个非常及时的问题是它们的经验估计。本文描述了一系列可以通过使用机器学习技术、使用多层人工神经网络(ANN)或所谓的深度网络来解决这一问题的研究。辛顿等人的开创性贡献。(Neural Comput18(7)：1527-1554,2006)提出了一种快速高效的训练算法--深度学习，此后机器学习取得了重大突破。经济学尚未从这些发展中受益，因此我们认为，现在是将多层神经网络和深度学习应用于经济学中的ABM的合适时机。
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.