Recommendations for probabilistic seismic hazard analysis: Guidance on uncertainty and use of experts

Budnitz, Robert J.; Apostolakis, G.; Boore, David M.

doi:10.2172/479072

Cited by 281 publications

(180 citation statements)

References 4 publications

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“…This classification of uncertainty is applied in numerous modeling domains [3,5,4,6]. Aleatory uncertainty, sometimes referred to as stochastic or random uncertainty, is that which is (as a practical matter) inherent in the system under study.…”

Section: Aleatory Versus Epistemic Uncertaintymentioning

confidence: 99%

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Unwin¹,

Eslinger²,

Johnson³

2012

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The Risk-Informed Safety Margin Characterization (RISMC) pathway is a set of activities defined under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability Program. The overarching objective of RISMC is to support plant life-extension decision-making by providing a state-of-knowledge characterization of safety margins in key systems, structures, and components (SSCs). A technical challenge at the core of this effort is to establish the conceptual and technical feasibility of analyzing safety margin in a risk-informed way, which, unlike conventionally defined deterministic margin analysis, would be founded on probabilistic characterizations of uncertainty in SSC performance.In the context of probabilistic risk assessment (PRA) technology, there has arisen a general consensus about the distinctive roles of two types of uncertainty: aleatory and epistemic, where the former represents irreducible, random variability inherent in a system, whereas the latter represents a state of knowledge uncertainty on the part of the analyst about the system which is, in principle, reducible through further research. While there is often some ambiguity about how any one contributing uncertainty in an analysis should be classified, there has nevertheless emerged a broad consensus on the meanings of these uncertainty types in the PRA setting. However, while RISMC methodology shares some features with conventional PRA, it will nevertheless be a distinctive methodology set. Therefore, the paradigms for classification of uncertainty in the PRA setting may not fully port to the RISMC environment. Yet the notion of risk-informed margin is based on the characterization of uncertainty, and it is therefore critical to establish a common understanding of uncertainty in the RISMC setting.The RISMC framework contrasts sharply with the PRA structure in that the underlying models are not inherently aleatory. Rather, they are largely deterministic physical/engineering models. However, there are uncertainties associated with appropriate quantification of many of the model input parameters. The current RISMC paradigm for uncertainty quantification is to adopt the criteria by which epistemic and aleatory uncertainties are distinguished in PRAs (irreducibility, whether the source is random variability, etc.) as the basis for classifying input parameter uncertainties. However, since the underlying structure of RISMC is deterministic and not aleatory, and (almost) all input parameters are purely deterministic, judging whether a given input uncertainty should be viewed as aleatory or deterministic presents more of a challenge. Note that this ambiguity is a well-recognized issue, even in the context of conventional PRA. However, a viewpoint sometimes expressed is that if this ambiguity does not affect the insights from a study relevant to decision-making, then it is unimportant. Our intent in this report is to assess the robustness of study insights to alternative categorizations of uncertainty -addressing the question "does it ma...

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Section: Aleatory Versus Epistemic Uncertaintymentioning

confidence: 99%

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Unwin¹,

Eslinger²,

Johnson³

2012

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“…Going beyond these classical concepts, other taxonomies have sought to address the uncertainties associated with use of physical, engineering, and social models to predict the behavior of complex, inhomogeneous, self-interacting systems (e.g., Paté-Cornell 1996;Budnitz et al 1997). In some technical disciplines, uncertainty types and the means by which they are characterized have become so well established that uncertainty analysis techniques have become standardized and sometimes even proceduralized (ANS/IEEE/NRC 1983;NRC 1990;Budnitz et al 1997).…”

Section: Taxonomies Of Uncertaintymentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

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SummaryThis white paper describes the results of new research to develop an uncertainty characterization (UC) process to help address the challenges of regional climate change mitigation and adaptation decisions. This research is being carried out as part of the integrated Regional Earth System Model (iRESM) initiative, a new scientific framework developed at Pacific Northwest National Laboratory to evaluate the interactions between human and environmental systems and mitigation and adaptation decisions at regional scales. The framework integrates a regional climate model; a regional energyeconomy model; and highly spatially-resolved models of crop productivity, building energy demands, electricity infrastructure operation and expansion, and water supply and management. The iRESM framework is intended to help regional stakeholders (scientists as well as decision makers) understand the consequences of climate change as well as the consequences of policies to mitigate or adapt to such change within regions.The initiative has developed the following four science questions to guide its research:• How do intrinsic regional characteristics shape, enhance, or constrain regional mitigation and adaptation opportunities?• How do projected changes in mean climate versus climate extremes affect the development of adaptation and mitigation strategies?• How might interactions between management decisions and natural processes contribute to rapid or nonlinear changes, and do they contribute to climate feedbacks?• How will adaptation and mitigation strategies interact in the next few decades in terms of achieving their respective goals?An important consideration for the iRESM initiative is that Earth system mechanisms and future changes are imperfectly understood and in some cases deeply uncertain-especially at the level of resolution required for regional analyses and decision making. The UC process developed for the initiative addresses uncertainty by first identifying through sensitivity analysis the key uncertainties in data inputs, individual model structures, and coupled models that are important for particular stakeholder questions and evaluation criteria. These key uncertainties are then characterized and propagated to determine the robustness of the framework's results for the particular questions, thereby providing insights for researchers and decision makers alike. The process differs from many traditional applications of uncertainty quantification (UQ) because of its focus on stakeholder needs and its allowance for qualitative and semi-quantitative methods for describing uncertainty. The process not only permits the dimensionality of the UQ problem to be reduced, it also allows research efforts to be targeted at the uncertainties that really matter for the question in hand.This decision-specific orientation for UC has multiple implications for the iRESM initiative that will continue to be explored, including: the importance of stakeholder interactions and the development of methods for communicating results; the dev...

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“…These include questions as to the way in which the responses elicited may be affected by (i) the choice of elicitation method within a specific context (Bolger & Rowe, 2014Cooke, 1991); (ii) the selection and number of experts (Aspinall, 2010); (iii) experts' personal attributes (Budnitz et al, 1997;Morgan, 2014); and (iv) the presentation of relevant information in order to overcome biases (Martin et al, 2012;Morgan, 2014). The judgmental forecasting context offers a good platform from which to study such issues, given the apparently conflicting research findings on the contribution of expertise .…”

Section: Introductionmentioning

confidence: 99%

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Alvarado

Barrero

Önkal

et al. 2017

International Journal of Forecasting

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a b s t r a c tExpert knowledge elicitation lies at the core of judgmental forecasting-a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50-50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster's relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.

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Recommendations for probabilistic seismic hazard analysis: Guidance on uncertainty and use of experts

Cited by 281 publications

References 4 publications

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

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