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Judgement or models: The importance of task differences

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  • Lawrence, M.
  • O'Connor, M.

Abstract

Over twenty years research on cognitive biases and limitations has built a strong case for managers not to use their judgement if a suitable formula or model is available. Much of this work was built on research demonstrating that in an environment where the judgemental cues are well established, pre-specified and not auto-correlated and where there is little contextual information, that a model based on expert judgement (a model-of-man) is more accurate than the expert himself. However, much practical decision making takes place in a setting where cues are not well established, where auto-correlation is present, and where there is general contextual information. This study investigated decisions made in such a setting, namely time series forecasting. Forecasts were estimated judgementally for 111 real life time series. These estimates were then used to construct a model-of-man and the forecasts from this model compared in accuracy with the original judgemental forecasts. This study found the model-of-man not to be superior to man when assessed in terms of forecast accuracy thus demonstrating a commonly occurring task setting in which the dominant research result in not true.

Suggested Citation

  • Lawrence, M. & O'Connor, M., 1996. "Judgement or models: The importance of task differences," Omega, Elsevier, vol. 24(3), pages 245-254, June.
  • Handle: RePEc:eee:jomega:v:24:y:1996:i:3:p:245-254
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    References listed on IDEAS

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    Cited by:

    1. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
    2. Patrick Krieger & Carsten Lausberg, 2021. "Entscheidungen, Entscheidungsfindung und Entscheidungsunterstützung in der Immobilienwirtschaft: Eine systematische Literaturübersicht [Decisions, decision-making and decisions support systems in r," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 7(1), pages 1-33, April.
    3. Goodwin, Paul, 2000. "Improving the voluntary integration of statistical forecasts and judgment," International Journal of Forecasting, Elsevier, vol. 16(1), pages 85-99.
    4. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, vol. 25(1), pages 35-47.
    5. 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.
    6. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    7. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    8. Jorgensen, Magne, 2007. "Forecasting of software development work effort: Evidence on expert judgement and formal models," International Journal of Forecasting, Elsevier, vol. 23(3), pages 449-462.
    9. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.

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