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BY 4.0 license Open Access Published by De Gruyter Open Access October 8, 2019

Escape from model-land

  • Erica L. Thompson and Leonard A. Smith
From the journal Economics


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.

JEL Classification: C52; C53; C6; D8; D81


Bank of England (2019). May 2019 inflation report. in Google Scholar

Berger, J.O., and Smith, L.A. (2018). On the statistical formalism of uncertainty quantification, Annual Reviews of Statistics and its Application, 6: 433–460. in Google Scholar

Beven, K., Buytaert, W., and Smith, L.A. (2012). On virtual observatories and modelled realities (or why discharge must be treated as a virtual variable). Hydrological Processes, 26(12): 1905–1908. in Google Scholar

Beven, K.J., and Lane, S. (2019). Invalidation of models and fitness-for-purpose: a rejectionist approach. In Beisbart, C. and N.J. Saam (eds.), Computer simulation validation. Fundamental concepts, methodological frameworks, and philosophical perspectives. Springer, Cham.10.1007/978-3-319-70766-2_6Search in Google Scholar

Beven, K.J. (2019b). Towards a new paradigm for testing models as hypotheses in the inexact sciences. Proceedings of the Royal Society A, 475(2224): 20180862. in Google Scholar

Bröcker, J., and Smith, L.A. (2008). From ensemble forecasts to predictive distribution functions. Tellus A, 60(4): 663–678. in Google Scholar

Cooke, R.M. (1991). Experts in uncertainty; opinion and subjective probability in science.Oxford University Press, New York, Oxford.Search in Google Scholar

Frigg, R., Smith, L.A., and Stainforth, D.A. (2015). An assessment of the foundational assumptions in high-resolution climate projections: the case of UKCP09. Synthese, 192(12): 3979–4008. in Google Scholar

Good, I.J. (1959). Kinds of probability. Science, 129(3347): 443–447. in Google Scholar

IPCC (2013). Summary for policymakers. In Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA.Search in Google Scholar

Judd, K., Reynolds, C.A., Rosmand, T.E., and Smith, L.A. (2008). The geometry of model error. Journal of Atmospheric Sciences, 65(6): 1749–1772.Search in Google Scholar

Judd, K., and Smith, L.A. (2001). Indistinguishable states I. Perfect model scenario. Physica D, 151(2–4): 125–141. in Google Scholar

Judd, K., and Smith, L.A. (2004). Indistinguishable states II: the imperfect model scenario. Physica D, 196(3–4): 224–242. in Google Scholar

Kalnay, E. (2003). Atmospheric modeling, data assimilation and predictability. Cambridge University Press.Search in Google Scholar

Lorenz, E.N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences. 20(2): 130–141.<0130:DNF>2.0.CO;2Search in Google Scholar

Mayo, D.G. (1996). Error and the growth of experimental knowledge (science and its conceptual foundations series). University of Chicago Press.Search in Google Scholar

Parker, W. (2009). Confirmation and adequacy-for-purpose in climate modelling. Proceedings of the Aristotelian Society, Supplementary Volume, 83(1): 233–249. in Google Scholar

Parker, W. (2019). Model evaluation: an adequacy for purpose view. Philosophy of Science. ForthcomingSearch in Google Scholar

Petersen, A.C. (2012). Simulating nature: a philosophical study of computer-simulation uncertainties and their role in climate science and policy advice. Chapman and Hall/CRC.10.1201/b11914Search in Google Scholar

Smale, S (1966). Structurally stable systems are not dense. American Journal of Mathematics, 88(2): 491–496.Search in Google Scholar

Smith, L.A. (1995). Accountability and error in ensemble forecasting. In 1995 ECMWF Seminar on Predictability. Vol. 1, 351–368. ECMWF, Reading.Search in Google Scholar

Smith, L.A. (2000). Disentangling uncertainty and error: on the predictability of nonlinear systems. In Mees, A.I. (ed.), Nonlinear dynamics and statistics. Boston: Birkhauser.Search in Google Scholar

Smith, L.A. (2002). What might we learn from climate forecasts? Proceedings of the National Academy of Sciences of the United States of America, 4(99): 2487–2492. in Google Scholar

Smith, L.A. (2006). Predictability past predictability present. In Palmer T., and R. Hagedorn (eds.), Predictability of weather and climate. Cambridge University Press, Cambridge, UK.10.1017/CBO9780511617652.010Search in Google Scholar

Smith, L.A. (2007). Chaos: a very short introduction. Oxford University Press, Oxford.10.1093/actrade/9780192853783.001.0001Search in Google Scholar

Smith, L.A. (2016). Integrating information, misinformation and desire: improved weather-risk management for the energy sector. In Aston, P.J., A.J. Mullholland, and K.M.M. Tant (eds.), UK success stories in industrial mathematics. Springer.10.1007/978-3-319-25454-8_37Search in Google Scholar

Smith, L.A., and Petersen, A.C. (2014). Variations on reliability: connecting climate predictions to climate policy. In Boumans, M., G. Hon, and A.C. Petersen (eds.), Error and uncertainty in scientific practice. Pickering & Chatto, London.Search in Google Scholar

Smith, L.A., and Stern, N. (2011). Uncertainty in science and its role in climate policy. Philosophical Transactions of the Royal Society A, 369(1956): 4818–4841. in Google Scholar

Thompson, E. (2013). Modelling North Atlantic storms in a changing climate. PhD thesis, Imperial College, London.Search in Google Scholar

Thompson, E., Frigg, R., and Helgeson, C. (2016). Expert judgment for climate change adaptation. Philosophy of Science 83(5):1110–1121. in Google Scholar

Thompson E.L., and Smith L.A. (2019). Informing anticipatory humanitarian action: a framework for using forecasts effectively. In preparation.Search in Google Scholar

Tuckett, D. (2011). Minding the markets: an emotional finance view of financial instability. Springer.Search in Google Scholar

Tuckett, D., and Nikolic, M. (2017). The role of conviction and narrative in decision-making under radical uncertainty. Theory & Psychology, 27(4): 501–523. in Google Scholar

Tuckett, D., and Taffler, R. (2008). Phantastic objects and the financial market’s sense of reality: A psychoanalytic contribution to the understanding of stock market instability. The International Journal of Psychoanalysis, 89(2): 389–412. in Google Scholar

Whitehead, A.N. (1925). Science and the modern world: Lowell lectures, 1925. New American Library, (1956).Search in Google Scholar

Received: 2019-03-05
Revised: 2019-07-25
Accepted: 2019-09-16
Published Online: 2019-10-08
Published in Print: 2019-12-01

© 2019 Erica L. Thompson et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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