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Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

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  • Emanuele Borgonovo

Abstract

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)). Results and indications an analyst derives depend on the method selected for the study. In this work, we investigate the assumptions at the basis of various indicator families to discuss the information they convey to the analyst/decisionmaker. We start with nonparametric techniques, and then present variance‐based methods. By means of an example we show that output variance does not always reflect a decisionmaker state of knowledge of the inputs. We then examine the use of moment‐independent approaches to global sensitivity analysis, i.e., techniques that look at the entire output distribution without a specific reference to its moments. Numerical results demonstrate that both moment‐independent and variance‐based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.

Suggested Citation

  • Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
  • Handle: RePEc:wly:riskan:v:26:y:2006:i:5:p:1349-1361
    DOI: 10.1111/j.1539-6924.2006.00806.x
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    1. Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Uncertainty Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1299-1312, October.
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