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Structured Analogies for Forecasting

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Author Info

  • J.S. Armstrong

    (The Wharton School)

Abstract

When people forecast, they often use analogies but in an unstructured manner. We propose a structured judgmental procedure that involves asking experts to list as many analogies as they can, rate how similar the analogies are to the target situation, and match the outcomes of the analogies with possible outcomes of the target. An administrator would then derive a forecast from the experts information. We compared structured analogies with unaided judgments for predicting the decisions made in eight conflict situations. These were difficult forecasting problems; the 32% accuracy of the unaided experts was only slightly better than chance. In contrast, 46% of structured analogies forecasts were accurate. Among experts who were independently able to think of two or more analogies and who had direct experience with their closest analogy, 60% of forecasts were accurate. Collaboration did not improve accuracy.

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File URL: http://128.118.178.162/eps/get/papers/0502/0502001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series General Economics and Teaching with number 0502001.

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Length: 34 pages
Date of creation: 04 Feb 2005
Date of revision:
Handle: RePEc:wpa:wuwpgt:0502001

Note: Type of Document - pdf; pages: 34
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Web page: http://128.118.178.162

Related research

Keywords: accuracy; analogies; collaboration; conflict; expert; forecasting; judgment.;

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References

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  1. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics.
  2. Green, Kesten C., 2002. "Forecasting decisions in conflict situations: a comparison of game theory, role-playing, and unaided judgement," International Journal of Forecasting, Elsevier, vol. 18(3), pages 321-344.
  3. Robert Goldfarb & H. O. Stekler & Joel David, 2005. "Methodological issues in forecasting: Insights from the egregious business forecast errors of late 1930," Journal of Economic Methodology, Taylor & Francis Journals, vol. 12(4), pages 517-542.
  4. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
  5. Daniel Kahneman & Dan Lovallo, 1993. "Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking," Management Science, INFORMS, vol. 39(1), pages 17-31, January.
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Citations

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Cited by:
  1. Green, Kesten C. & Armstrong, J. Scott, 2009. "Role thinking: Standing in other people’s shoes to forecast decisions in conflicts," MPRA Paper 16422, University Library of Munich, Germany.
  2. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
  3. Akrivi LITSA & Fotios PETROPOULOS & Konstantinos NIKOLOPOULOS, 2012. "Forecasting the Success of Governmental "Incentivized" Initiatives: Case Study of a New Policy Promoting the Replacement of Old Household; Air-conditioners," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 2(1), pages 15, February.
  4. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
  5. Savio, Nicolas D. & Nikolopoulos, Konstantinos, 2013. "A strategic forecasting framework for governmental decision-making and planning," International Journal of Forecasting, Elsevier, vol. 29(2), pages 311-321.
  6. Green, Kesten C., 2008. "Assessing probabilistic forecasts about particular situations," MPRA Paper 8836, University Library of Munich, Germany.
  7. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
  8. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.

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