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Escape from model-land

Author

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  • Thompson, Erica L.
  • Smith, Leonard A.

Abstract

Both mathematical modelling and simulation methods in general have contributed greatly to understanding, insight and forecasting in many fields including macroeconomics. Nevertheless, we must remain careful to distinguish model-land and model-land quantities from the real world. Decisions taken in the real world are more robust when informed by estimation of real-world quantities with transparent uncertainty quantification, than when based on 'optimal' model-land quantities obtained from simulations of imperfect models optimized, perhaps optimal, in model-land. The authors present a short guide to some of the temptations and pitfalls of model-land, some directions towards the exit, and two ways to escape. Their aim is to improve decision support by providing relevant, adequate information regarding the real-world target of interest, or making it clear why today's model models are not up to that task for the particular target of interest.

Suggested Citation

  • Thompson, Erica L. & Smith, Leonard A., 2019. "Escape from model-land," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-17.
    • Handle: RePEc:zbw:ifweej:201940
    DOI: 10.5018/economics-ejournal.ja.2019-40
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    References listed on IDEAS

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    1. David Tuckett, 2011. "Minding the Markets," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-30782-7, December.
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    Cited by:

    1. Joel Katzav & Erica L. Thompson & James Risbey & David A. Stainforth & Seamus Bradley & Mathias Frisch, 2021. "On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives," Climatic Change, Springer, vol. 169(1), pages 1-20, November.
    2. Sangha, Laljeet & Shortridge, Julie, 2023. "Quantification of unreported water use for supplemental crop irrigation in humid climates using publicly available agricultural data," Agricultural Water Management, Elsevier, vol. 287(C).
    3. Tuckett, David & Holmes, Douglas & Pearson, Alice & Chaplin, Graeme, 2020. "Monetary policy and the management of uncertainty: a narrative approach," Bank of England working papers 870, Bank of England.
    4. Glette-Iversen, Ingrid & Aven, Terje, 2021. "On the meaning of and relationship between dragon-kings, black swans and related concepts," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    5. Charlie Wilson & Céline Guivarch & Elmar Kriegler & Bas Ruijven & Detlef P. Vuuren & Volker Krey & Valeria Jana Schwanitz & Erica L. Thompson, 2021. "Evaluating process-based integrated assessment models of climate change mitigation," Climatic Change, Springer, vol. 166(1), pages 1-22, May.
    6. Ryan O’Loughlin, 2024. "Why we need lower-performance climate models," Climatic Change, Springer, vol. 177(2), pages 1-20, February.
    7. Marina Baldissera Pacchetti & Suraje Dessai & David A. Stainforth & Seamus Bradley, 2021. "Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018," Climatic Change, Springer, vol. 168(1), pages 1-25, September.

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    More about this item

    Keywords

    uncertainty; Modelling and simulation; decision-making; model evaluation; uncertainty; structural model error; dynamical systems; radical;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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