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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. Never-theless, 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 our best estimate of real-world quantities, than when "optimal" model-land quantities obtained from imperfect simulations are employed. 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.

Suggested Citation

  • Thompson, Erica L. & Smith, Leonard A., 2019. "Escape from model-land," Economics Discussion Papers 2019-23, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwedp:201923
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    File URL: http://www.economics-ejournal.org/economics/discussionpapers/2019-23
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    File URL: https://www.econstor.eu/bitstream/10419/194875/1/1662970102.pdf
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    References listed on IDEAS

    as
    1. Frigg, Roman & Smith, Leonard A. & Stainforth, David A., 2015. "An assessment of the foundational assumptions inhigh-resolution climate projections: the case of UKCP09," LSE Research Online Documents on Economics 61635, London School of Economics and Political Science, LSE Library.
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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

      modelling and simulation; decision-making; model evaluation; uncertainty; structural model error; dynamical systems; radical uncertainty;
      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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