The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Uncertainties in risk analysis: Six levels of treatment
Open accessAbstract
This paper examines different levels of analytical sophistication in the treatment of uncertainties in risk analysis, and the possibility of transfer of experience across fields of application. First, this paper describes deterministic and probabilistic methods of treatment of risk and uncertainties, and the different viewpoints that shape these analyses. Second, six different levels of treatment of uncertainty are presented and discussed in the light of the evolution of the risk management philosophy in the US. Because an in-depth treatment of uncertainties can be complex and costly, this paper then discusses when and why a full (two-tier) uncertainty analysis is justified. In the treatment of epistemic uncertainty, an unavoidable and difficult problem is the encoding of probability distributions based on scientific evidence and expert judgments. The last sections include a description of different approaches to the aggregation of expert opinions and their use in risk analysis, and a recent example of methodology and application (in seismic hazard analysis) that can be transferred to other domains.
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Cites
“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891
Simulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356
Cited by
A methodology for performing global uncertainty and sensitivity analysis in systems biology
Marino, S., Hogue, I. B., Ray, C. J., & Kirschner, D. E. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.04.011 · Cited by 2,999
Aleatory or epistemic? Does it matter?
Kiureghian, A. D., & Ditlevsen, O. (2009) · Structural Safety · doi:10.1016/j.strusafe.2008.06.020 · Cited by 2,454
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
Yang, J. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2010.10.007 · Cited by 326
Data Uncertainty Learning in Face Recognition
Chang, J., Lan, Z., Cheng, C., & Wei, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00575 · Cited by 320
On the assessment of uncertainty in risk diagrams
Goerlandt, F., & Reniers, G. (2016) · Safety Science · doi:10.1016/j.ssci.2015.12.001 · Cited by 148
Metadata
- Authors
- Paté-Cornell, M. E.
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 1996-11
- Volume
- 54
- Issue
- 2-3
- Pages
- 95-111
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W1978904760
- Cited by
- 811
- References
- 2
- FWCI
- 7.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Paté-Cornell, M. E. (1996). Uncertainties in risk analysis: Six levels of treatment. Reliability Engineering & System Safety. https://doi.org/10.1016/s0951-8320(96)00067-1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.