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Human Diagnostic Reasoning by Computer: An Illustration from Financial Analysis

Author

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  • Marinus J. Bouwman

    (University of Oregon)

Abstract

A major complaint of people who use "decision-making" computer programs is that these programs merely provide a final decision, and fail to present the supporting argumentation, in terms the user can understand. This article presents an approach that makes computer programs more "human-like" by basing them on human decision making behavior. Decision making processes of student financial analysts are captured by asking them to think aloud during their evaluation. These verbal traces, called protocols, are analyzed at various levels of detail, resulting in specific models of the decision making processes involved, the strategies used, and the task-specific (financial) knowledge that is required to perform the task. The models and strategies are translated into executable computer programs. Extensive comparisons between human behavior and model simulation output are provided, assessing the extent that the computer program "thinks" and "talks" like a human decision maker. Although the model clearly suffers from "linguistic rigidity," it does appear to perform the evaluation in a similar manner as the human decision maker, examining the same information in the same order, making the same inferences, and reporting the same conclusions.

Suggested Citation

  • Marinus J. Bouwman, 1983. "Human Diagnostic Reasoning by Computer: An Illustration from Financial Analysis," Management Science, INFORMS, vol. 29(6), pages 653-672, June.
  • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:6:p:653-672
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    File URL: http://dx.doi.org/10.1287/mnsc.29.6.653
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    Cited by:

    1. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.
    2. Kwok, Helen, 2002. "The effect of cash flow statement format on lenders' decisions," The International Journal of Accounting, Elsevier, vol. 37(3), pages 347-362.
    3. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    4. Vetschera, Rudolf, 1993. "Zur Konsistenz von Abweichungsanalysen in hierarchischen Kennzahlensystemen," Discussion Papers, Series I 269, University of Konstanz, Department of Economics.
    5. Feelders, A. J. & Daniels, H. A. M., 2001. "A general model for automated business diagnosis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 623-637, May.
    6. repec:spr:grdene:v:13:y:2004:i:2:d:10.1023_b:grup.0000021839.04093.5d is not listed on IDEAS
    7. Kida, Thomas & Smith, James F., 1995. "The encoding and retrieval of numerical data for decision making in accounting contexts: Model development," Accounting, Organizations and Society, Elsevier, vol. 20(7-8), pages 585-610.
    8. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    9. Siskos, Y. & Spyridakos, A., 1999. "Intelligent multicriteria decision support: Overview and perspectives," European Journal of Operational Research, Elsevier, vol. 113(2), pages 236-246, March.

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