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  1. The Problem of Measure Sensitivity Redux.Peter Brössel - 2013 - Philosophy of Science 80 (3):378-397.
    Fitelson (1999) demonstrates that the validity of various arguments within Bayesian confirmation theory depends on which confirmation measure is adopted. The present paper adds to the results set out in Fitelson (1999), expanding on them in two principal respects. First, it considers more confirmation measures. Second, it shows that there are important arguments within Bayesian confirmation theory and that there is no confirmation measure that renders them all valid. Finally, the paper reviews the ramifications that this "strengthened problem of measure (...)
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  • Bayesian Confirmation: A Means with No End.Peter Brössel & Franz Huber - 2015 - British Journal for the Philosophy of Science 66 (4):737-749.
    Any theory of confirmation must answer the following question: what is the purpose of its conception of confirmation for scientific inquiry? In this article, we argue that no Bayesian conception of confirmation can be used for its primary intended purpose, which we take to be making a claim about how worthy of belief various hypotheses are. Then we consider a different use to which Bayesian confirmation might be put, namely, determining the epistemic value of experimental outcomes, and thus to decide (...)
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  • Evidential Support, Transitivity, and Screening-Off.William Roche - 2015 - Review of Symbolic Logic 8 (4):785-806.
    Is evidential support transitive? The answer is negative when evidential support is understood as confirmation so that X evidentially supports Y if and only if p(Y | X) > p(Y). I call evidential support so understood “support” (for short) and set out three alternative ways of understanding evidential support: support-t (support plus a sufficiently high probability), support-t* (support plus a substantial degree of support), and support-tt* (support plus both a sufficiently high probability and a substantial degree of support). I also (...)
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  • New Axioms for Probability and Likelihood Ratio Measures.V. Crupi, N. Chater & K. Tentori - 2013 - British Journal for the Philosophy of Science 64 (1):189-204.
    Probability ratio and likelihood ratio measures of inductive support and related notions have appeared as theoretical tools for probabilistic approaches in the philosophy of science, the psychology of reasoning, and artificial intelligence. In an effort of conceptual clarification, several authors have pursued axiomatic foundations for these two families of measures. Such results have been criticized, however, as relying on unduly demanding or poorly motivated mathematical assumptions. We provide two novel theorems showing that probability ratio and likelihood ratio measures can be (...)
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  • Kuhn Vs. Popper on Criticism and Dogmatism in Science, Part II: How to Strike the Balance.Darrell P. Rowbottom - 2013 - Studies in History and Philosophy of Science Part A 44 (2):161-168.
    This paper is a supplement to, and provides a proof of principle of, Kuhn vs. Popper on Criticism and Dogmatism in Science: A Resolution at the Group Level. It illustrates how calculations may be performed in order to determine how the balance between different functions in science—such as imaginative, critical, and dogmatic—should be struck, with respect to confirmation (or corroboration) functions and rules of scientific method.
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  • Formal and Empirical Methods in Philosophy of Science.Vincenzo Crupi & Stephan Hartmann - 2009 - In Friedrich Stadler et al (ed.), The Present Situation in the Philosophy of Science. Springer. pp. 87--98.
    This essay addresses the methodology of philosophy of science and illustrates how formal and empirical methods can be fruitfully combined. Special emphasis is given to the application of experimental methods to confirmation theory and to recent work on the conjunction fallacy, a key topic in the rationality debate arising from research in cognitive psychology. Several other issue can be studied in this way. In the concluding section, a brief outline is provided of three further examples.
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  • Information Versus Knowledge in Confirmation Theory.Darrell Patrick Rowbottom - 2012 - Logique Et Analyse 226:137-149.
    I argue that so-called 'background knowledge' in confirmation theory has little, if anything, to do with 'knowledge' in the sense of mainstream epistemology. I argue that it is better construed as 'background information', which need not be believed in, justified, or true.
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  • Empirical Evidence Claims Are a Priori.Darrell Patrick Rowbottom - 2013 - Synthese 190 (14):2821-2834.
    This paper responds to Achinstein’s criticism of the thesis that the only empirical fact that can affect the truth of an objective evidence claim such as ‘e is evidence for h’ (or ‘e confirms h to degree r’) is the truth of e. It shows that cases involving evidential flaws, which form the basis for Achinstein’s objections to the thesis, can satisfactorily be accounted for by appeal to changes in background information and working assumptions. The paper also argues that the (...)
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  • Group Level Interpretations of Probability: New Directions.Darrell Patrick Rowbottom - 2013 - Pacific Philosophical Quarterly 94 (2):188-203.
    In this article, I present some new group level interpretations of probability, and champion one in particular: a consensus-based variant where group degrees of belief are construed as agreed upon betting quotients rather than shared personal degrees of belief. One notable feature of the account is that it allows us to treat consensus between experts on some matter as being on the union of their relevant background information. In the course of the discussion, I also introduce a novel distinction between (...)
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  • Confirmation Measures and Sensitivity.Olav B. Vassend - unknown
    Stevens draws a useful distinction between ordinal scales, interval scales, and ratio scales. Most recent discussions of confirmation measures have proceeded on the ordinal level of analysis. In this paper, I give a more quantitative analysis. In particular, I show that the requirement that our desired confirmation measure be at least an \emph{interval} measure naturally yields necessary conditions that jointly entail the log-likelihood measure. Thus I conclude that the log-likelihood measure is the only good candidate interval measure.
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