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

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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 

JEL Classification

C63 E03 E27 

Notes

Acknowledgements

This paper has benefited from discussions with Spyros Kousides, Nan Su and Herbert Dawid. Any remaining errors or omissions are the sole responsibility of the author.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Economic Theory and Computational Economics (ETACE), Department of Business Administration and EconomicsBielefeld UniversityBielefeldGermany

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