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Lessons for Decision-Analysis Practice from the Automotive Industry

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  • Robert F. Bordley

    (Integrative Systems + Design, College of Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Decision analysis is widely used in making decisions involving low-probability, high-consequence events (e.g., drug discovery, oil and gas drilling, risk reduction). This paper focuses on the automotive industries in which more intermediate uncertainties are important. As in any large organization, different members of the organization have different information and different incentives. In this setting, influence diagrams proved invaluable in identifying the information creditable models need, discovering new distinctions of high value, developing win/win compromises, and enabling higher-value technology transfer. However, these examples also highlight the need for more research on addressing motivational biases within organizations.

Suggested Citation

  • Robert F. Bordley, 2023. "Lessons for Decision-Analysis Practice from the Automotive Industry," Interfaces, INFORMS, vol. 53(3), pages 240-246, May.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:3:p:240-246
    DOI: 10.1287/inte.2022.1151
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    References listed on IDEAS

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    1. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    2. Bordley, Robert & Bier, Vicki, 2009. "Updating beliefs about variables given new information on how those variables relate," European Journal of Operational Research, Elsevier, vol. 193(1), pages 184-194, February.
    3. Bordley, Robert F, 1993. "Estimating Automotive Elasticities from Segment Elasticities and First Choice/Second Choice Data," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 455-462, August.
    4. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    5. Samuel E. Bodily & Michael S. Allen, 1999. "A Dialogue Process for Choosing Value-Creating Strategies," Interfaces, INFORMS, vol. 29(6), pages 16-28, December.
    6. Daniel Owen, 2015. "Collaborative Decision Making," Decision Analysis, INFORMS, vol. 12(1), pages 29-45, March.
    7. Apiruk Detwarasiti & Ross D. Shachter, 2005. "Influence Diagrams for Team Decision Analysis," Decision Analysis, INFORMS, vol. 2(4), pages 207-228, December.
    8. Judea Pearl, 2005. "Influence Diagrams---Historical and Personal Perspectives," Decision Analysis, INFORMS, vol. 2(4), pages 232-234, December.
    9. Michael W. Kusnic & Daniel Owen, 1992. "The Unifying Vision Process: Value beyond Traditional Decision Analysis in Multiple-Decision-Maker Environments," Interfaces, INFORMS, vol. 22(6), pages 150-166, December.
    10. Bordley, Robert F, 1989. "Generating Market Elasticity Estimates Using Cross-Sectional First-Choice and Second-Choice Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 141-146, January.
    11. John W. Seaman & John W. Seaman & James D. Stamey, 2012. "Hidden Dangers of Specifying Noninformative Priors," The American Statistician, Taylor & Francis Journals, vol. 66(2), pages 77-84, May.
    12. Ralph L. Keeney, 1982. "Feature Article—Decision Analysis: An Overview," Operations Research, INFORMS, vol. 30(5), pages 803-838, October.
    13. Robert F. Bordley, 2014. "Reference Class Forecasting: Resolving Its Challenge to Statistical Modeling," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 221-229, November.
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