Decomposition for Judgmental Forecasting and Estimation

  • Donald G. MacGregor

Abstract

Forecasters often need to estimate uncertain quantities, but with limited time and resources. Decomposition is a method for dealing with such problems by breaking down (decomposing) the estimation task into a set of components that can be more readily estimated, and then combining the component estimates to produce a target estimate. Estimators can effectively apply decomposition to either multiplicative or segmented forecasts, though multiplicative decomposition is especially sensitive to correlated errors in component values. Decomposition is most used for highly uncertain estimates, such as ones having a large numerical value (e.g., millions or more) or quantities in an unfamiliar metric. When possible, multiple estimators should be used and the results aggregated. In addition, multiple decompositions can be applied to the same estimation problem and the results resolved into a single estimate. Decomposition should be used only when the estimator can make component estimates more accurately or more confidently than the target estimate.

Keywords

Algorithmic decomposition judgmental forecasting numerical estimation 

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References

  1. Andradottir, S. & V. M. Bier (1997), “Choosing the number of conditioning events in judgmental forecasting,” Journal of Forecasting, 16, 255–286.CrossRefGoogle Scholar
  2. Arkes, H. R. (2001), “Overconfidence in judgmental forecasting,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.Google Scholar
  3. Armstrong, J. S. (1985), Long-Range Forecasting: From Crystal Ball to Computer (2nd ed.) New York: John Wiley & Sons. (Full text at http://www.hops.wharton.upenn.edu/forecast.)
  4. Armstrong, J. S. & J. G. Andress (1970), “Exploratory analysis of marketing data: Trees vs. regression,” Journal of Marketing Research,7, 487–492. (Full text at http://www.hops.wharton.upenn.edu/forecast.)CrossRefGoogle Scholar
  5. Armstrong, J. S., W. B. Denniston & M. M. Gordon (1975), “The use of the decomposition principle in making judgments,” Organizational Behavior and Human Performance, 14, 257–263.CrossRefGoogle Scholar
  6. Bonner, S. E., R. Libby & M. W. Nelson (1996), “Using decision aids to improve auditors’ conditional probability judgments,” The Accounting Review, 71, 221–240.Google Scholar
  7. Connolly, T. & D. Dean (1997), “Decomposed versus holistic estimates of effort required for software writing tasks,” Management Science, 43, 1029–1045.CrossRefGoogle Scholar
  8. Dangerfield, B. J. & J. S. Morris (1992), “Top-down or bottom-up: Aggregate versus dis-aggregate extrapolations,” International Journal of Forecasting, 8, 233–241.CrossRefGoogle Scholar
  9. Dawes, R. M. (1975), “The mind, the model, and the task,” in F. Restie, R. M. Shiffron, N. J. Castellan, H. R. Lindman & D. B. Pisoni (eds.), Cognitive Theory. (Vol. 1, ), Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 119–129.Google Scholar
  10. Dawes, R. M. (1979), “The robust beauty of improper linear models in decision making,” American Psychologist, 34, 571–582.CrossRefGoogle Scholar
  11. Dawes, R. M. & B. Corrigan (1974), “Linear models in decision making,” Psychological Bulletin, 81, 95–106.CrossRefGoogle Scholar
  12. Dunn, D. M., W. H. William & W. A. Spivey (1971), “Analysis and prediction of telephone demand in local geographic areas,” Bell Journal of Economics and Management Science, 2, 561–576.CrossRefGoogle Scholar
  13. Edmundson, R. H. (1990), “Decomposition: A strategy for judgmental forecasting,” Journal of Forecasting, 9, 305–314.CrossRefGoogle Scholar
  14. Edwards, W. & D. von Winterfeldt (1986), Decision Analysis and Behavioral Research. New York: Cambridge University Press.Google Scholar
  15. Goldberg, L. R. (1968), “Simple models or simple processes? Some research on clinical judgments,” American Psychologist, 23, 483–496.CrossRefGoogle Scholar
  16. Goldberg, L. R. (1970), “Man vs. model of man: A rationale, plus some evidence, for a method of improving on clinical inferences,” Psychological Bulletin, 73, 422–432CrossRefGoogle Scholar
  17. Gordon, T. P., J. S. Morris & B. J. Dangerfield (1997), “Top-down or bottom-up: Which is the best approach to forecasting?” Journal of Business Forecasting, 16, 13–16Google Scholar
  18. Harvey, N. (2001), “Improving judgment in forecasting,” in J. S. Armstrong (ed.), Princi-ples of Forecasting. Norwell, MA: Kluwer Academic Publishers.Google Scholar
  19. Henrion, M., G. W. Fischer & T. Mullin (1993), “Divide and conquer? Effects of decomposition on the accuracy and calibration of subjective probability distributions,” Organizational Behavior and Human Decision Processes, 55, 207–227.CrossRefGoogle Scholar
  20. Hora, S. C., N. G. Dodd & J. A. Hora (1993), “The use of decomposition in probability assessments of continuous variables,” Journal of Behavioral Decision Making, 6, 133–147.CrossRefGoogle Scholar
  21. Kahneman, D., P. Slovic & A. Tversky (1982), Judgment Under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.Google Scholar
  22. Kleinmuntz, D. N., M. G. Fennema & M. E. Peecher (1996), “Conditioned assessment of subjective probabilities: Identifying the benefits of decomposition Organizational Behavior and Human Decision Processes,66, 1–15.Google Scholar
  23. MacGregor, D. G. & J. S. Armstrong (1994), “Judgmental decomposition: When does it work?” International Journal of Forecasting, 10,495–506. (Full text at http://www.hops.wharton.upenn.edu/forecast.)CrossRefGoogle Scholar
  24. MacGregor, D. G. & S. Lichtenstein (1991), “Problem structuring aids for quantitative estimation,” Journal of Behavioral Decision Making, 4, 101–116.CrossRefGoogle Scholar
  25. MacGregor, D. G., S. Lichtenstein & P. Slovic (1988), “Structuring knowledge retrieval: An analysis of decomposed quantitative judgments,” Organizational Behavior and Human Decision Processes, 42, 303–323.CrossRefGoogle Scholar
  26. Meehl, P. E. (1957), “When shall we use our heads instead of the formula?” Journal of Counseling Psychology, 4, 268–273.CrossRefGoogle Scholar
  27. Menon, G. (1997), “Are the parts better than the whole? The effects of decompositional questions on judgments of frequent behaviors,” Journal of Marketing Research, 34, 335–346.CrossRefGoogle Scholar
  28. Plous, S. (1993), The Psychology of Judgment and Decision Making. New York: McGraw-Hill.Google Scholar
  29. Raiffa, H. (1968), Decision Analysis. Reading, MA: Addison-Wesley.Google Scholar
  30. Slovic, P. & S. Lichtenstein (1971), “Comparison of Bayesian and regression approaches to the study of information processing in judgment,” Organizational Behavior and Human Performance, 6, 649–744.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Donald G. MacGregor
    • 1
  1. 1.Decision ResearchEugeneUSA

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