IDEAS home Printed from https://ideas.repec.org/p/zbw/ifwedp/201923.html
   My bibliography  Save this paper

Escape from model-land

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.economics-ejournal.org/economics/discussionpapers/2019-23
    Download Restriction: no
    File URL: https://www.econstor.eu/bitstream/10419/194875/1/1662970102.pdf
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. Ryan O’Loughlin, 2024. "Why we need lower-performance climate models," Climatic Change, Springer, vol. 177(2), pages 1-20, February.
    6. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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

      NEP fields

      This paper has been announced in the following NEP Reports:

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:ifwedp:201923. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.html .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.