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The philosophical significance of Cox’s theorem
 International Journal of Approximate Reasoning
, 2004
"... Cox’s theorem states that, under certain assumptions, any measure of belief is isomorphic to a probability measure. This theorem, although intended as a justification of the subjectivist interpretation of probability theory, is sometimes presented as an argument for more controversial theses. Of par ..."
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Cox’s theorem states that, under certain assumptions, any measure of belief is isomorphic to a probability measure. This theorem, although intended as a justification of the subjectivist interpretation of probability theory, is sometimes presented as an argument for more controversial theses. Of particular interest is the thesis that the only coherent means of representing uncertainty is via the probability calculus. In this paper I examine the logical assumptions of Cox’s theorem and I show how these impinge on the philosophical conclusions thought to be supported by the theorem. I show that the more controversial thesis is not supported by Cox’s theorem.
Generalized Uncertain Databases: First Steps
 In Proceedings of the Workshop on Management of Uncertain Data (MUD
, 2010
"... Abstract. Existing uncertain databases have difficulty managing data when exact confidence values or probabilities are not available. Confidence values may be known imprecisely or coarsely, or even be missing altogether. We propose a generalized uncertain database that can manage data with such in ..."
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Abstract. Existing uncertain databases have difficulty managing data when exact confidence values or probabilities are not available. Confidence values may be known imprecisely or coarsely, or even be missing altogether. We propose a generalized uncertain database that can manage data with such incomplete knowledge of uncertainty. We develop a semantics for generalized uncertain databases based on DempsterShafer theory. We propose a representation scheme for generalized uncertain databases that generalizes the Trio representation. Our approach builds upon Trio’s query processing techniques to extend them to operate on generalized uncertain databases. 1
unknown title
, 2012
"... A methodology for exploiting the tolerance for imprecision in genetic fuzzy systems and its application to characterization of rotor blade leading edge materials ..."
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A methodology for exploiting the tolerance for imprecision in genetic fuzzy systems and its application to characterization of rotor blade leading edge materials
Contents lists available at ScienceDirect Methods in Oceanography
"... journal homepage: www.elsevier.com/locate/mio Full length article Analysis of causation of loss of communication ..."
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journal homepage: www.elsevier.com/locate/mio Full length article Analysis of causation of loss of communication
TOWARDS A BAYESIAN THEORY OF SECONDORDER UNCERTAINTY: LESSONS FROM NONSTANDARD LOGICS
"... Abstract. Secondorder uncertainty, also known as model uncertainty and Knightian uncertainty, arises when decisionmakers can (partly) model the parameters of their decision problems. It is widely believed that subjective probability, and more generally Bayesian theory, are illsuited to represent a ..."
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Abstract. Secondorder uncertainty, also known as model uncertainty and Knightian uncertainty, arises when decisionmakers can (partly) model the parameters of their decision problems. It is widely believed that subjective probability, and more generally Bayesian theory, are illsuited to represent a number of interesting secondorder uncertainty features, especially “ignorance ” and “ambiguity”. This failure is sometimes taken as an argument for the rejection of the whole Bayesian approach, triggering a Bayes vs antiBayes debate which is in many ways analogous to what the classical vs nonclassical debate used to be in logic. This paper attempts to unfold this analogy and suggests that the development of nonstandard logics offers very useful lessons on the contextualisation of justified norms of rationality. By putting those lessons to work I will flesh out an epistemological framework suitable for extending the expressive power of standard Bayesian norms of rationality to secondorder uncertainty in a way which is both formally and foundationally conservative.
www.mdpi.com/journal/ijerph Perspectives to Performance of Environment and Health Assessments and Models—From Outputs to Outcomes?
, 2013
"... Abstract: The calls for knowledgebased policy and policyrelevant research invoke a need to evaluate and manage environment and health assessments and models according to their societal outcomes. This review explores how well the existing approaches to assessment and model performance serve this ne ..."
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Abstract: The calls for knowledgebased policy and policyrelevant research invoke a need to evaluate and manage environment and health assessments and models according to their societal outcomes. This review explores how well the existing approaches to assessment and model performance serve this need. The perspectives to assessment and model performance in the scientific literature can be called: (1) quality assurance/control, (2) uncertainty analysis, (3) technical assessment of models, (4) effectiveness and (5) other perspectives, according to what is primarily seen to constitute the goodness of assessments and models. The categorization is not strict and methods, tools and frameworks in different perspectives may overlap. However, altogether it seems that most approaches to assessment and model performance are relatively narrow in their scope. The focus in most approaches is on the outputs and making of assessments and models. Practical application of the outputs and the consequential outcomes are often left unaddressed. It appears that more comprehensive approaches that combine the essential characteristics of different perspectives are needed. This necessitates a better account of the mechanisms of collective knowledge creation and the relations between knowledge and practical action. Some new
Conceptual and Practical Aspects of Quantifying Uncertainty in Environmental
"... Abstract: Environmental decision support intends to use the best available scientific knowledge to predict the consequences of management alternatives. This raises 3 questions: (i) How to formally represent and quantify scientific knowledge? (ii) How to find adequate model structures and parameter ..."
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Abstract: Environmental decision support intends to use the best available scientific knowledge to predict the consequences of management alternatives. This raises 3 questions: (i) How to formally represent and quantify scientific knowledge? (ii) How to find adequate model structures and parameter values for predicting the behaviour of environmental systems under different driving conditions? (iii) How to implement efficient numerical procedures to actually calculate such predictions? Approaches to address all three of these questions will briefly be discussed. With respect to (i) an intersubjective interpretation of probabilities with an extension to imprecise probabilities is suggested as the most adequate representation of scientific knowledge. Conceptual arguments in favour of this approach are discussed as well as problems of alternative approaches. To address (ii) the importance of considering input errors, model structure deficiencies, and internal stochasticity of the modelled system is emphasized, as well as handling remaining systematic errors or