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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.

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Notes

  1. Even though we have to generate quite a lot of training data from the computationally heavy simulation model, this is necessary only once to train the surrogate model. Once the surrogate is in place, it can be use to replace the heavy model, precisely as the term “surrogate” would seem to imply. Note that the computational complexity of training the surrogate model on the training data might turn out to be quite high. It therefore seems reasonable to assume that this method is only applicable to a subclass of ABMs that benefit from using such emulators. I thank a referee for this observation.

  2. A possible candidate for such environmentally-aware, cognitive algorithms would be Hutter’s AIXI, see Hutter (2000).

  3. But not impossible in principle. For example, the global sensitivity analysis of a large-scale ABM such as the Eurace@Unibi Model was already performed using HPC clusters (Barde and van der Hoog 2017). Also, empirical validation is currently being done for medium-sized ABMs, and given the exponential increase in computing power is expected to yield results in the coming years.

  4. A side-remark must be made here on the computational complexity of the neural network training. Since it is not uncommon that ANNs have millions of weights that must be trained, this is by no means a simple feat.

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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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Correspondence to Sander van der Hoog.

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Financial support from the Horizon 2020 ISIGrowth Project (Innovation-fuelled, Sustainable, Inclusive Growth), under Grant No. 649186, is gratefully acknowledged.

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van der Hoog, S. Surrogate Modelling in (and of) Agent-Based Models: A Prospectus. Comput Econ 53, 1245–1263 (2019). https://doi.org/10.1007/s10614-018-9802-0

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