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Decision making and planning under low levels of predictability

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  • Makridakis, Spyros
  • Taleb, Nassim

Abstract

This special section aims to demonstrate the limited predictability and high level of uncertainty in practically all important areas of our lives, and the implications of this. It summarizes the huge body of solid empirical evidence accumulated over the past several decades that proves the disastrous consequences of inaccurate forecasts in areas ranging from the economy and business to floods and medicine. The big problem is, however, that the great majority of people, decision and policy makers alike, still believe not only that accurate forecasting is possible, but also that uncertainty can be reliably assessed. Reality, however, shows otherwise, as this special section proves. This paper discusses forecasting accuracy and uncertainty, and distinguishes three distinct types of predictions: those relying on patterns for forecasting, those utilizing relationships as their basis, and those for which human judgment is the major determinant of the forecast. In addition, the major problems and challenges facing forecasters and the reasons why uncertainty cannot be assessed reliably are discussed using four large data sets. There is also a summary of the eleven papers included in this special section, as well as some concluding remarks emphasizing the need to be rational and realistic about our expectations and avoid the common delusions related to forecasting.

Suggested Citation

  • Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:716-733
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    References listed on IDEAS

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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
    3. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
    4. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    5. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
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    Cited by:

    1. Bouckaert, Nicolas & Van den Heede, Koen & Van de Voorde, Carine, 2018. "Improving the forecasting of hospital services: A comparison between projections and actual utilization of hospital services," Health Policy, Elsevier, vol. 122(7), pages 728-736.
    2. Phillips, Christina Jane & Nikolopoulos, Konstantinos, 2019. "Forecast quality improvement with Action Research: A success story at PharmaCo," International Journal of Forecasting, Elsevier, vol. 35(1), pages 129-143.
    3. Christopher D. Ittner & Jeremy Michels, 2017. "Risk-based forecasting and planning and management earnings forecasts," Review of Accounting Studies, Springer, vol. 22(3), pages 1005-1047, September.
    4. Estrada, Fernando, 2011. "Theory of financial risk," MPRA Paper 29665, University Library of Munich, Germany.
    5. Jan Anne Annema & Hugo Priemus, 2013. "Mega-projects: new challenges to cope with climate change and energy transition," Chapters, in: Hugo Priemus & Bert van Wee (ed.), International Handbook on Mega-Projects, chapter 18, pages 398-417, Edward Elgar Publishing.
    6. Miller, Craig & Newell, Barry, 2013. "Framing integrated research to address a dynamically complex issue: The red headed cockchafer challenge," Agricultural Systems, Elsevier, vol. 117(C), pages 13-18.
    7. Taleb, Nassim Nicholas, 2019. "How much data do you need? An operational, pre-asymptotic metric for fat-tailedness," International Journal of Forecasting, Elsevier, vol. 35(2), pages 677-686.
    8. Vera Ivanyuk, 2022. "Proposed Model of a Dynamic Investment Portfolio with an Adaptive Strategy," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    9. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    10. Len Fisher & Anders Sandberg, 2022. "A Safe Governance Space for Humanity: Necessary Conditions for the Governance of Global Catastrophic Risks," Global Policy, London School of Economics and Political Science, vol. 13(5), pages 792-807, November.
    11. Paltrinieri, Nicola & Tugnoli, Alessandro & Cozzani, Valerio, 2015. "Hazard identification for innovative LNG regasification technologies," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 18-28.
    12. Andreas Rauch & Willem Hulsink, 2023. "Just one Damned Thing After Another: Towards an Event-based Perspective of Entrepreneurship," Entrepreneurship Theory and Practice, , vol. 47(3), pages 662-681, May.
    13. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    14. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    15. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    16. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    17. Konstantinos V. Katsikopoulos, 2011. "Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice," Decision Analysis, INFORMS, vol. 8(1), pages 10-29, March.
    18. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    19. Estrada, Fernando, 2009. "Tamaño y Riesgo en los Mercados Financieros [Size and Risk in the Finanzal Markets]," MPRA Paper 19267, University Library of Munich, Germany.
    20. Puhr, Harald & Müllner, Jakob, 2022. "Foreign to all but fluent in many: The effect of multinationality on shock resilience," Journal of World Business, Elsevier, vol. 57(6).
    21. Ludovic Gaudard & Franco Romerio, 2015. "Natural hazard risk in the case of an emergency: the real options’ approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 473-488, January.
    22. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.

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