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Structured Analogies for Forecasting

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  • 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.

Suggested Citation

  • J.S. Armstrong, 2005. "Structured Analogies for Forecasting," General Economics and Teaching 0502001, EconWPA.
  • Handle: RePEc:wpa:wuwpgt:0502001
    Note: Type of Document - pdf; pages: 34
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/get/papers/0502/0502001.pdf
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    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. 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.
    5. 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.
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    Cited by:

    1. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    2. 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 1-15, February.
    3. 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.
    4. repec:spr:manint:v:49:y:2009:i:2:d:10.1007_s11575-008-0138-1 is not listed on IDEAS
    5. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    6. 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.
    7. Green, Kesten C., 2008. "Assessing probabilistic forecasts about particular situations," MPRA Paper 8836, University Library of Munich, Germany.
    8. 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.
    9. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    10. Green, Kesten C. & Armstrong, J. Scott, 2011. "Role thinking: Standing in other people's shoes to forecast decisions in conflicts," International Journal of Forecasting, Elsevier, vol. 27(1), pages 69-80, January.
    11. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.
    12. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    13. 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.
    14. 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.
    15. Wright, Malcolm J. & Stern, Philip, 2015. "Forecasting new product trial with analogous series," Journal of Business Research, Elsevier, vol. 68(8), pages 1732-1738.
    16. repec:jdm:journl:v:12:y:2017:i:4:p:369-381 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • A - General Economics and Teaching

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