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

Sander Hoog ()
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Sander Hoog: Bielefeld University

Computational Economics, 2019, vol. 53, issue 3, No 16, 1245-1263

Abstract: 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. (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.

Keywords: Surrogate modelling; Agent-based models; Estimation (search for similar items in EconPapers)
JEL-codes: C63 E03 E27 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10614-018-9802-0

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