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Javelin Diagrams: A Graphical Tool for Probabilistic Sensitivity Analysis

Author

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  • James C. Felli

    (Naval Postgraduate School, Defense Resources Management Institute (DRMI), Monterey, California 93943)

  • Gordon B. Hazen

    (Northwestern University, Evanston, Illinois 60208)

Abstract

To demonstrate post hoc robustness of decision problems to parameter estimates, analysts may conduct a probabilistic sensitivity analysis , assigning distributions to uncertain parameters and computing the probability of decision change. In contrast to classical threshold proximity methods of sensitivity analysis, no appealing graphical methods are available to present the results of a probabilistic sensitivity analysis. Here we introduce an analog of tornado diagrams for probabilistic sensitivity analysis, which we call javelin diagrams . Javelin diagrams are graphical augmentations of tornado diagrams displaying both the probability of decision change and the information value associated with individual parameters or parameter sets. We construct javelin diagrams for simple problems, discuss their properties, and illustrate their realistic application via a probabilistic sensitivity analysis of a seven-parameter decision analysis from the medical literature.

Suggested Citation

  • James C. Felli & Gordon B. Hazen, 2004. "Javelin Diagrams: A Graphical Tool for Probabilistic Sensitivity Analysis," Decision Analysis, INFORMS, vol. 1(2), pages 93-107, June.
  • Handle: RePEc:inm:ordeca:v:1:y:2004:i:2:p:93-107
    DOI: 10.1287/deca.1030.0006
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    References listed on IDEAS

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    Cited by:

    1. Borgonovo, Emanuele & Marinacci, Massimo, 2015. "Decision analysis under ambiguity," European Journal of Operational Research, Elsevier, vol. 244(3), pages 823-836.
    2. L. Robin Keller & Ali Abbas & Manel Baucells & Vicki M. Bier & David Budescu & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Ahti Salo & George Wu, 2010. "From the Editors..," Decision Analysis, INFORMS, vol. 7(4), pages 327-330, December.
      • L. Robin Keller & Manel Baucells & Kevin F. McCardle & Gregory S. Parnell & Ahti Salo, 2007. "From the Editors..," Decision Analysis, INFORMS, vol. 4(4), pages 173-175, December.
      • L. Robin Keller & Manel Baucells & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Gregory S. Parnell & Ahti Salo, 2008. "From the Editors..," Decision Analysis, INFORMS, vol. 5(4), pages 173-176, December.
      • L. Robin Keller & Manel Baucells & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Gregory S. Parnell & Ahti Salo, 2009. "From the Editors ..," Decision Analysis, INFORMS, vol. 6(4), pages 199-201, December.
    3. L. Robin Keller, 2008. "From the Editor..," Decision Analysis, INFORMS, vol. 5(3), pages 113-115, September.
    4. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.
    5. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    6. Ali E. Abbas & David V. Budescu & Hsiu-Ting Yu & Ryan Haggerty, 2008. "A Comparison of Two Probability Encoding Methods: Fixed Probability vs. Fixed Variable Values," Decision Analysis, INFORMS, vol. 5(4), pages 190-202, December.
    7. Gordon Hazen & Emanuele Borgonovo & Xuefei Lu, 2023. "Information Density in Decision Analysis," Decision Analysis, INFORMS, vol. 20(2), pages 89-108, June.
    8. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    9. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.

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