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

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  • Sander Hoog

    (Bielefeld University)

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.

Suggested Citation

  • Sander Hoog, 2019. "Surrogate Modelling in (and of) Agent-Based Models: A Prospectus," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1245-1263, March.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:3:d:10.1007_s10614-018-9802-0
    DOI: 10.1007/s10614-018-9802-0
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
    2. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
    3. Vandin, Andrea & Giachini, Daniele & Lamperti, Francesco & Chiaromonte, Francesca, 2022. "Automated and distributed statistical analysis of economic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    4. Yi Zhang & Zhe Li & Yongchao Zhang, 2020. "Validation and Calibration of an Agent-Based Model: A Surrogate Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-9, January.
    5. Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2021. "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," Papers 2102.05405, arXiv.org, revised Nov 2023.
    6. Alperen Bektas & Valentino Piana & René Schumann, 2021. "A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model," SN Business & Economics, Springer, vol. 1(6), pages 1-25, June.
    7. Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2020. "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," LEM Papers Series 2020/31, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

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    More about this item

    Keywords

    Surrogate modelling; Agent-based models; Estimation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E03 - Macroeconomics and Monetary Economics - - General - - - Behavioral Macroeconomics
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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