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Decision making and planning under low levels of predictability: Enhancing the scenario method

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  • Wright, George
  • Goodwin, Paul
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    Abstract

    In this paper we review and analyse scenario planning as an aid to anticipation of the future under conditions of low predictability. We examine how successful the method is in mitigating issues to do with inappropriate framing, cognitive and motivational bias, and inappropriate attributions of causality. Although we demonstrate that the scenario method contains weaknesses, we identify a potential for improvement. Four general principles that should help to enhance the role of scenario planning when predictability is low are discussed: (i) challenging mental frames, (ii) understanding human motivations, (iii) augmenting scenario planning through adopting the approach of crisis management, and (iv) assessing the flexibility, diversity, and insurability of strategic options in a structured option-against-scenario evaluation.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-4WFPPCG-2/2/26e24030f3d9d3f7cfabb60c87664e13
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 25 (2009)
    Issue (Month): 4 (October)
    Pages: 813-825

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    Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:813-825

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Scenario planning Crisis management Framing Judgment Heuristics and biases Low predictability Rare events;

    References

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    1. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    2. Robert Fildes & Paul Goodwin, 2007. "Good and Bad Judgment in Forecasting: Lessons from Four Companies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 5-10, Fall.
    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. Paul Goodwin, 2001. "Enhancing Strategy Evaluation in Scenario Planning: a Role for Decision Analysis," Journal of Management Studies, Wiley Blackwell, vol. 38(1), pages 1-16, 01.
    5. Wright, George, 2002. "Game theory, game theorists, university students, role-playing and forecasting ability," International Journal of Forecasting, Elsevier, vol. 18(3), pages 383-387.
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    Citations

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    Cited by:
    1. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    2. Klenk, Nicole L. & Hickey, Gordon M., 2011. "A virtual and anonymous, deliberative and analytic participation process for planning and evaluation: The Concept Mapping Policy Delphi," International Journal of Forecasting, Elsevier, vol. 27(1), pages 152-165, January.
    3. Wright, George & Rowe, Gene, 2011. "Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda," International Journal of Forecasting, Elsevier, vol. 27(1), pages 1-13, January.
    4. ├ľnkal, Dilek & Lawrence, Michael & Zeynep SayIm, K., 2011. "Influence of differentiated roles on group forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 27(1), pages 50-68, January.
    5. 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.

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