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Group-based forecasting?: A social psychological analysis

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  • Kerr, Norbert L.
  • Tindale, R. Scott

Abstract

The potential of group (vs. individual) forecasting is analyzed from the perspective of the social psychology of groups. The social decision scheme theory (SDST) is summarized, and several simulations are presented to demonstrate the dependence of group aggregation accuracy upon factors such as group size, the accuracy and distribution of individual forecasts, and shared representations of the forecasting problem. Many advantages and disadvantages of group aggregation are identified and related to four generic methods of group aggregation (statistical aggregation, prediction markets, the Delphi method, and face-to-face discussion). A number of aspects of forecasting problems are identified which should govern whether or not group forecasting can be relied upon, and if so, what aggregation method should be used.

Suggested Citation

  • Kerr, Norbert L. & Tindale, R. Scott, 2011. "Group-based forecasting?: A social psychological analysis," International Journal of Forecasting, Elsevier, vol. 27(1), pages 14-40, January.
  • Handle: RePEc:eee:intfor:v:27:y::i:1:p:14-40
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    References listed on IDEAS

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    Cited by:

    1. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    2. 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.
    3. Kauko, Karlo & Palmroos, Peter, 2014. "The Delphi method in forecasting financial markets— An experimental study," International Journal of Forecasting, Elsevier, vol. 30(2), pages 313-327.
    4. repec:wsi:apjorx:v:34:y:2017:i:01:n:s0217595917400012 is not listed on IDEAS
    5. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    6. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    7. Makkonen, Marika & Hujala, Teppo & Uusivuori, Jussi, 2016. "Policy experts' propensity to change their opinion along Delphi rounds," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 61-68.
    8. repec:eee:intfor:v:27:y:2011:i:1:p:1-13 is not listed on IDEAS
    9. Agrawal, Rashmi & Nanda, S.K. & Rao, D. Rama & Rao, B.V.L.N., 2013. "Integrated Approach to Human Resource Forecasting: An Exercise in Agricultural Sector," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 26(2).
    10. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
    11. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.
    12. Aurélien Baillon & Han Bleichrodt & Ning Liu & Peter P. Wakker, 2016. "Group decision rules and group rationality under risk," Journal of Risk and Uncertainty, Springer, vol. 52(2), pages 99-116, April.

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