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Deep Learning in (and of) Agent-Based Models: A Prospectus

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  • Sander van der Hoog

Abstract

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. (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 Deep Learning and multi-layered neural networks to agent-based models in economics.

Suggested Citation

  • Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
  • Handle: RePEc:arx:papers:1706.06302
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    3. Sylvain Barde & Sander van Der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Working Papers hal-03458672, HAL.
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