A Fuzzy Set Theoretic Foundation for Vagueness in Uncertainty Analysis

Unwin, Stephen D.

doi:10.1111/j.1539-6924.1986.tb00191.x

Cited by 27 publications

(13 citation statements)

References 3 publications

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“…The correlation is more signifiant if the coefficient is closer to ± 1. We may consider our values to belong to fuzzy categories [10], and then the correlations show tendencies that may be considered significant even for values beyond 0.7 when compared with lower values. The possibilities to pair the strings arc very large.…”

Section: Methodsmentioning

confidence: 99%

Correlations between the Clinical Picture and the Intentionality in Schizophrenic and Schizoaffective Patients

Marin¹

1995

Psychopathology

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The results are presented of catamnestic research on 24 patients with the diagnosis of schizophrenia at the first clinical episode confirmed according to the ICD-10 criteria. The clinical picture rated with the Positive and Negative Symptoms Scale (Kay, Fiszbein and Opler, 1987) and with the BPRS, was correlated with the intentionality, which was rated with the InSka (IntentionalitätsSkala; Mundt, 1985). The transformation of the phenomenal significance into a numeric value enables the use of mathematical methods for the search of interactions inside the schizophrenic phenomenon. We found the following significant tendencies: (1) the negative syndrome score correlates with the residual intentionality; (2) the negative syndrome score correlates with the premorbid intentionality; (3) the clinical thought disturbance correlates with the premorbid speech content intentional profile.

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Section: Methodsmentioning

confidence: 99%

Correlations between the Clinical Picture and the Intentionality in Schizophrenic and Schizoaffective Patients

Marin¹

1995

Psychopathology

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“…This metric is viewed as a more credible measure to elicit from experts than an opinion on the relative likelihood of a given parameter value. So, while there are some conceptual and theoretical connections between possibility and probability measures (Unwin 1986;Dubois 2006), the assignment of possibilities does not demand probabilistic thinking.…”

Section: Possibility Theorymentioning

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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“…Probabilities, interpreted in a classical frequentist sense, provide a natural framework in which to accommodate stochastic/aleatory uncertainty. While non-probabilistic approaches to characterizing epistemic uncertainty have been proposed and sometimes adopted [7][8][9][10][11], probability theory (interpreted in a Bayesian rather than frequentist sense) remains the most widely applied framework for modeling epistemic uncertainty. In the following subsection, the practical, modeling implications of these distinctions are outlined.…”

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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A Fuzzy Set Theoretic Foundation for Vagueness in Uncertainty Analysis

Cited by 27 publications

References 3 publications

Correlations between the Clinical Picture and the Intentionality in Schizophrenic and Schizoaffective Patients

Correlations between the Clinical Picture and the Intentionality in Schizophrenic and Schizoaffective Patients

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

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

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