Deep Learning in Agent-Based Models: A Prospectus

19 Pages Posted: 6 Jan 2016

Date Written: January 4, 2016

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.

Keywords: Deep Learning, Agent-based Models, Estimation, Meta-modelling

Suggested Citation

van der Hoog, Sander, Deep Learning in Agent-Based Models: A Prospectus (January 4, 2016). Bielefeld Working Papers in Economics and Management No. 02-2016, Available at SSRN: https://ssrn.com/abstract=2711216 or http://dx.doi.org/10.2139/ssrn.2711216

Sander Van der Hoog (Contact Author)

Bielefeld University ( email )

Universitätsstraße 25
Bielefeld, 33615
Germany

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