Presence Of Epistemic Uncertainties Research Articles

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

An 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.

PROTECTING BUILT PROPERTY AGAINST FIRE DISASTERS: MULTI ‐ATTRIBUTE DECISION MAKING WITH RESPECT TO FIRE RISK

The protection of buildings against fire disasters may require a comparison of alternative fire safety designs. The fire safety solutions can be compared by means of a general methodology known as multi‐attribute selection or multi‐criteria decision making. The alternative fire designs can be described by a number of attributes which characterise each of the alternatives. Fire risk expressed in the general form used for the quantitative risk assessment is applied to compose the set of attributes of a multi‐attribute selection problem. It is shown how to accomplish the multi‐attribute selection in the presence of epistemic uncertainties in the elements of fire risk estimate. Epistemic probability distributions assigned to elements of fire risk are specified and propagated though models of the multi‐attribute selection by means of Monte Carlo simulation. An example presented in the paper considers the choice among alternative systems of automatic fire sprinklers. Santruka Norint užtikrinti pastatu gaisrine sauga, gali prireikti lyginti alternatyvius projektinius saugos sprendimus. Tai atlikti galima pasitelkiant daugiakriterinio vertinimo metodologija. Alternatyvieji sprendimai gali būti aprašyti keletu charakteristiku (atributu) ir lyginami vienas su kitu. Straipsnyje atributu sarašas sudaromas naudojant gaisro rizikos išraiška, sudaroma kiekybinio rizikos vertinimo principais. Parodyta, kaip atlikti daugiakriterini vertinima, kai rizikos išraiškos elementai yra neapibrežti epistemine prasme. Episteminio neapibrežtumo skirstiniai priskiriami uždavinio atributams ir propaguojami matematiniais daugiakriterinio vertinimo modeliais pasitelkiant Monte Karlo modeliavima. Pateikiamas pavyzdys, nagrinejantis automatiniu sprinkleriu sistemos parinkima iš keliu alternatyviu variantu.

Representation of analysis results involving aleatory and epistemic uncertainty

Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behaviour of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary CDFs (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (e.g. interval analysis, possibility theory, evidence theory or probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterisations of epistemic uncertainty.

Multiattribute Selection from Alternative Designs of Infrastructure Components for Accidental Situations

Abstract: The design of infrastructure systems for “accidental” situations, i.e., abnormal service conditions, may include a comparison of alternative design solutions of system components. These solutions are compared by means of applying a universal methodology known as a multiattribute selection (MAS). The alternative designs are described by a number of attributes that characterize each of them. Failure probability and quantitative measure of the risk are used as safety‐related attributes of the alternative designs. This article shows how to carry out the MAS in the presence of epistemic uncertainties in safety‐related attributes. Distributions of epistemic uncertainties are specified by means of methods used for quantitative risk assessment. The MAS is carried out as a propagation of these uncertainties. The Monte Carlo simulation is used to propagate uncertainties through the algorithm of MAS. The approach proposed in this article is illustrated by two examples, which consider alternative designs of a protective barrier and alternative sprinkler systems.

A method for ranking components importance in presence of epistemic uncertainties

Importance Measures (IMs) are used to rank the contributions of components or basic events to the system performance, e.g. its reliability or risk. Most times, IMs are calculated without due account of the uncertainties in the model of the behavior of the system. The objective of this work is to investigate how uncertainties can influence IMs and to develop a method for giving them due account in the corresponding ranking of the components or basic events. The uncertainties considered in this work affect the model parameters values and are assumed to be described by probability density functions. The method for ranking the contributors to the system performance measure is applied to the auxiliary feedwater system of a nuclear pressurized water reactor.

Epistemic Uncertainty in the Ranking and Categorization of Probabilistic Safety Assessment Model Elements: Issues and Findings

In this work, we study the effect of epistemic uncertainty in the ranking and categorization of elements of probabilistic safety assessment (PSA) models. We show that, while in a deterministic setting a PSA element belongs to a given category univocally, in the presence of epistemic uncertainty, a PSA element belongs to a given category only with a certain probability. We propose an approach to estimate these probabilities, showing that their knowledge allows to appreciate "the sensitivity of component categorizations to uncertainties in the parameter values" (U.S. NRC Regulatory Guide 1.174). We investigate the meaning and utilization of an assignment method based on the expected value of importance measures. We discuss the problem of evaluating changes in quality assurance, maintenance activities prioritization, etc. in the presence of epistemic uncertainty. We show that the inclusion of epistemic uncertainly in the evaluation makes it necessary to evaluate changes through their effect on PSA model parameters. We propose a categorization of parameters based on the Fussell-Vesely and differential importance (DIM) measures. In addition, issues in the calculation of the expected value of the joint importance measure are present when evaluating changes affecting groups of components. We illustrate that the problem can be solved using DIM. A numerical application to a case study concludes the work.