Extending Monte Carlo Simulations to Represent and Propagate Uncertainties in Presence of Incomplete Knowledge: Application to the Transfer of a Radionuclide in the Environment

Baccou, Jean; Chojnacki, E.; Mercat-Rommens, C.; Baudrit, Cédric

doi:10.1061/(asce)0733-9372(2008)134:5(362)

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Li et al (2008) used a fuzzy-stochastic modelling approach for estimating health risks from air pollution. Baccou et al (2008) applied joint propagation methods for assessing the risk of radionuclide migration in the environment. Kentel and Aral (2005) compared 2D Monte Carlo and joint fuzzy and Monte Carlo propagation for calculating health risks, while Kentel (2006) applied such joint methods to groundwater resource management.…”

Section: Introductionmentioning

confidence: 99%

Risk-informed decision-making in the presence of epistemic uncertainty

Dubois

Guyonnet²

2011

International Journal of General Systems

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International audienceAn important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte-Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we therefore propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decision-maker's attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process

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Section: Introductionmentioning

confidence: 99%

Risk-informed decision-making in the presence of epistemic uncertainty

Dubois

Guyonnet²

2011

International Journal of General Systems

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“…or with few data? Answering these questions will require better exploring expert elicitation methods, such as the possibility approach, defining knowledge in the form of intervals (Baccou et al, 2008), or the Bayesian inference, enabling the modeller to combine prior information and new data into a posterior information (Hosseini et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Evaluating variability and uncertainty in radiological impact assessment using SYMBIOSE

Simon-Cornu

Beaugelin-Seiller

Boyer

et al. 2015

Journal of Environmental Radioactivity

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Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

Kang

Zhang

Zeng

et al. 2016

Chinese Journal of Aeronautics

111

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In this paper, a systematic review of non-probabilistic reliability metrics is conducted to assist the selection of appropriate reliability metrics to model the influence of epistemic uncertainty. Five frequently used non-probabilistic reliability metrics are critically reviewed, i.e., evidence-theory-based reliability metrics, interval-analysis-based reliability metrics, fuzzy-interval-analysis-based reliability metrics, possibility-theory-based reliability metrics (posbist reliability) and uncertainty-theory-based reliability metrics (belief reliability). It is pointed out that a qualified reliability metric that is able to consider the effect of epistemic uncertainty needs to (1) compensate the conservatism in the estimations of the component-level reliability metrics caused by epistemic uncertainty, and (2) satisfy the duality axiom, otherwise it might lead to paradoxical and confusing results in engineering applications. The five commonly used non-probabilistic reliability metrics are compared in terms of these two properties, and the comparison can serve as a basis for the selection of the appropriate reliability metrics

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Extending Monte Carlo Simulations to Represent and Propagate Uncertainties in Presence of Incomplete Knowledge: Application to the Transfer of a Radionuclide in the Environment

Cited by 5 publications

References 19 publications

Risk-informed decision-making in the presence of epistemic uncertainty

Risk-informed decision-making in the presence of epistemic uncertainty

Evaluating variability and uncertainty in radiological impact assessment using SYMBIOSE

Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

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