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Assessment of Cost Uncertainties for Large Technology Projects: A Methodology and an Application

Author

Listed:
  • Robin L. Dillon

    (McDonough School of Business, Georgetown University, Washington, DC 20057)

  • Richard John

    (Department of Psychology, University of Southern California, Los Angeles, California 90089)

  • Detlof von Winterfeldt

    (School of Policy, Planning, and Development, University of Southern California, Los Angeles, California 90089)

Abstract

Large projects, especially those planned and managed by government agencies, often incur substantial cost overruns. The tolerance, particularly on the part of members of Congress, for these cost overruns has decreased, thus increasing the need for accurate, defensible cost estimates. Important aspects of creating responsible cost estimates are accounting for the uncertainties in these estimates, expressing the estimates clearly, and communicating them to decision makers. Our method for estimating cost uncertainties can be used at all stages of a project. It combines the principles of probabilistic risk analysis with procedures for expert elicitation to incorporate uncertainties and extraordinary events in cost estimates. The Department of Energy implemented this process to select a new tritium supply source. During this implementation, we identified four key issues in modeling cost risks: how to consider correlations among cost components, how to aggregate assessments of multiple experts, how to manage communication and information sharing among experts, and what is an appropriate discount rate for cost estimates.

Suggested Citation

  • Robin L. Dillon & Richard John & Detlof von Winterfeldt, 2002. "Assessment of Cost Uncertainties for Large Technology Projects: A Methodology and an Application," Interfaces, INFORMS, vol. 32(4), pages 52-66, August.
    • Handle: RePEc:inm:orinte:v:32:y:2002:i:4:p:52-66
    DOI: 10.1287/inte.32.4.52.56
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    References listed on IDEAS

    as
    1. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    2. Detlof von Winterfeldt & Eric Schweitzer, 1998. "An Assessment of Tritium Supply Alternatives in Support of the US Nuclear Weapons Stockpile," Interfaces, INFORMS, vol. 28(1), pages 92-112, February.
    3. Robert T. Clemen & Gregory W. Fischer & Robert L. Winkler, 2000. "Assessing Dependence: Some Experimental Results," Management Science, INFORMS, vol. 46(8), pages 1100-1115, August.
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    Cited by:

    1. Edouard Kujawski & Mariana L. Alvaro & William R. Edwards, 2004. "Incorporating psychological influences in probabilistic cost analysis," Systems Engineering, John Wiley & Sons, vol. 7(3), pages 195-216.
    2. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    3. Edouard Kujawski & Gregory A. Miller, 2007. "Quantitative risk‐based analysis for military counterterrorism systems," Systems Engineering, John Wiley & Sons, vol. 10(4), pages 273-289, December.
    4. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.

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    Keywords

    Decision analysis: risk; Government;

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