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David Atkinson (2012). Confirmation and Justification. A Commentary on Shogenji's Measure.

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  1.  39
    Foundations of a Probabilistic Theory of Causal Strength.Jan Sprenger - 2018 - Philosophical Review 127 (3):371-398.
    This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...)
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  2. Skepticism and Epistemic Closure: Two Bayesian Accounts.Luca Moretti & Tomoji Shogenji - 2017 - International Journal for the Study of Skepticism 7 (1):1-25.
    This paper considers two novel Bayesian responses to a well-known skeptical paradox. The paradox consists of three intuitions: first, given appropriate sense experience, we have justification for accepting the relevant proposition about the external world; second, we have justification for expanding the body of accepted propositions through known entailment; third, we do not have justification for accepting that we are not disembodied souls in an immaterial world deceived by an evil demon. The first response we consider rejects the third intuition (...)
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  3.  48
    Inductive Logic.Vincenzo Crupi - 2015 - Journal of Philosophical Logic 44 (6):641-650.
    The current state of inductive logic is puzzling. Survey presentations are recurrently offered and a very rich and extensive handbook was entirely dedicated to the topic just a few years ago [23]. Among the contributions to this very volume, however, one finds forceful arguments to the effect that inductive logic is not needed and that the belief in its existence is itself a misguided illusion , while other distinguished observers have eventually come to see at least the label as “slightly (...)
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  4.  26
    Two Impossibility Results for Measures of Corroboration.Jan Sprenger - 2015 - British Journal for the Philosophy of Science:axw016.
    According to influential accounts of scientific method, such as critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. But despite the popularity of hypothesis tests in statistical inference and science in general, their philosophical foundations remain shaky. In particular, the interpretation of non-significant results—those that do not reject the tested hypothesis—poses a major philosophical challenge. To what extent do they corroborate the tested hypothesis, or provide a reason to accept it? Popper sought for measures of corroboration that could (...)
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  5.  46
    Assessing Theories: The Coherentist Approach.Peter Brössel - 2014 - Erkenntnis 79 (3):593-623.
    In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...)
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    State of the Field: Measuring Information and Confirmation.Vincenzo Crupi & Katya Tentori - 2014 - Studies in History and Philosophy of Science Part A 47:81-90.
  7.  15
    Information, Confirmation, and Conditionals.Peter Milne - 2014 - Journal of Applied Logic 12 (3):252-262.
  8.  23
    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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