Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

13 Pages Posted: 7 Nov 2006

See all articles by Emanuele Borgonovo

Emanuele Borgonovo

Bocconi University - Department of Decision Sciences

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

Borgonovo, Emanuele, Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches. Risk Analysis, Vol. 26, No. 5, pp. 1349-1361, October 2006. Available at SSRN: https://ssrn.com/abstract=943290 or http://dx.doi.org/10.1111/j.1539-6924.2006.00806.x

Emanuele Borgonovo (Contact Author)

Bocconi University - Department of Decision Sciences ( email )

Via Roentgen 1
Milan, 20136
Italy

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