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  1. Probability in Logic. [REVIEW]R. A. A. - 1957 - Review of Metaphysics 11 (2):348-348.
  2. On the Logic of High Probability.Ernest W. Adams - 1986 - Journal of Philosophical Logic 15 (3):255 - 279.
  3. Applying the Jeffrey Decision Model to Rational Betting and Information Acquisition.Ernest W. Adams & Roger D. Rosenkrantz - 1980 - Theory and Decision 12 (1):1-20.
  4. Shogenji's Probabilistic Measure of Coherence is Incoherent.K. Akiba - 2000 - Analysis 60 (4):356-359.
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  5. Shogenji's Probabilistic Measure of Coherence is Incoherent.Ken Akiba - 2000 - Analysis 60 (4):356–359.
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  6. Coherence as a Heuristic.Staffan Angere - 2008 - Mind 117 (465):1-26.
    The impossibility results of Bovens and Hartmann (2003) and Olsson (2005) call into question the strength of the connection between coherence and truth. As part of the inquiry into this alleged link, I define a notion of degree of truth-conduciveness, relevant for measuring the usefulness of coherence measures as rules-of-thumb for assigning probabilities in situations of partial knowledge. I use the concept to compare the viability of some of the measures of coherence that have been suggested so far under different (...)
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  7. The Defeasible Nature of Coherentist Justification.Staffan Angere - 2007 - Synthese 157 (3):321 - 335.
    The impossibility results of Bovens and Hartmann (2003, Bayesian epistemology. Oxford: Clarendon Press) and Olsson (2005, Against coherence: Truth, probability and justification. Oxford: Oxford University Press.) show that the link between coherence and probability is not as strong as some have supposed. This paper is an attempt to bring out a way in which coherence reasoning nevertheless can be justified, based on the idea that, even if it does not provide an infallible guide to probability, it can give us an (...)
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  8. Probability and Causality. [REVIEW]Frank Arntzenius - 1990 - Philosophy of Science 57 (2):338-340.
    This is a book review of J. H. Fetzer (ed) Probability and Causality.
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  9. Confirmation and Probability: A Reply to Settle.Patricia Baillie - 1971 - British Journal for the Philosophy of Science 22 (3):285-286.
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  10. Second-Order Probabilities and Belief Functions.Jonathan Baron - 1987 - Theory and Decision 23 (1):25-36.
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  11. Probability in Logic, Mathematics and Science.M. S. Bartlett - 1949 - Dialectica 3 (1‐2):104-113.
    Historically the emergence of a precise technical meaning for probability, as distinct from its vague popular useage, has taken time; and confusion still arises from the concept of probability having different meanings in different flelds of discourse. Its technical meaning and appropriate rules are surveyed in the flelds of logic , mathematics , and science , and the relation between these three aspects of probability theory discussed. ‐. M. S. B.
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  12. The One Systematically Ambiguous Concept of Probability.William H. Baumer - 1967 - Philosophy and Phenomenological Research 28 (2):264-268.
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  13. The Causal Roots of Probability.Marianne Belis - 2007 - In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. pp. 5--295.
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  14. A World of Probability. [REVIEW]Eleanor Bisbee - 1938 - Philosophy of Science 5 (3):360 - 366.
  15. Is There an "Anomalous" Section of the Laffer Curve?Walter Block - 2010 - Libertarian Papers 2.
    When an economy is at the upper part of the Laffer curve, a reduction in tax rates will, somewhat paradoxically, lead to a rise in the amount of money, both relatively and absolutely, the taxpayer will retain, but, also, to an increase in government revenues collected. The former result is a welcome one, from the libertarian perspective, not so the latter. Does this example exhibit a slight anomaly for the free enterprise philosophy , or does it furnish a true conundrum. (...)
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  16. On a Defect in the Customary Logical Formulation of Inductive Reasoning.Bernard Bosanquet - 1910 - Proceedings of the Aristotelian Society 11:29 - 40.
  17. Scientific Uncertainty: A User's Guide.Seamus Bradley - 2012 - Grantham Institute on Climate Change Discussion Paper.
    There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.
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  18. Practical Reasoning Through Coherent Goal Specification.Gerry Curtis Bridgeman - 2001 - Dissertation, Vanderbilt University
    In this work, I try to further specify what practical coherence should amount to. Any account of practical reasoning ought to be able to say something about how we ought to go about specifying our goals. One possibility is coherence theory. But coherence theory as it is normally conceived cannot be sufficient for rationality. Practical reasoning, which takes place over time, poses special difficulties for the coherence theorist. There is a danger that unless coherence theory has some element of stability (...)
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  19. ESP: Extrasensory Perception or Effect of Subjective Probability?Peter Brugger & Kirsten I. Taylor - 2003 - Journal of Consciousness Studies 10 (6-7):6-7.
    This paper consists of two parts. In the first, we discuss the neuropsychological correlates of belief in a 'paranormal' or magical causation of coincidences. In particular, we review experimental evidence demonstrating that believers in ESP and kindred forms of paranormal phenomena differ from disbelievers with respect to indices of sequential response production and semantic-associative processing. Not only do believers judge artificial coincidences as more 'meaningful' than disbelievers, they also more strongly suppress coincidental productions (i.e. repetitions) in their generation of random (...)
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  20. Probability as a Determiner of Rat Behavior.E. Brunswik - 1939 - Journal of Experimental Psychology 25 (2):175.
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  21. A Subjective Appraisal of the Probability of Happenings Related by Children From 8 to 15 Years of Age.Franciszek Bujak - 1975 - Roczniki Filozoficzne 23 (4):120.
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  22. Probability Logic.John P. Burgess - 1969 - Journal of Symbolic Logic 34 (2):264-274.
  23. Preferences Representable by a Lower Expectation: Some Characterizations. [REVIEW]Andrea Capotorti, Giulianella Coletti & Barbara Vantaggi - 2008 - Theory and Decision 64 (2-3):119-146.
    We propose two different characterizations for preference relations representable by lower (upper) expectations with the aim of removing either fair price or completeness requirements. Moreover, we give an explicit characterization for comparative degrees of belief on a finite algebra of events representable by lower probabilities.
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  24. Probability and Content Measure.Rudolf Carnap - 1966 - In Paul K. Feyerabend & Grover Maxwell (eds.), Mind, Matter and Method: Essays in Philosophy and Science in Honor of Herbert Feigl. University of Minnesota Press: Minneapolis. pp. 248--260.
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  25. Preventative Scope in Causal Inference.C. D. Carroll & P. W. Cheng - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 833--838.
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  26. Amounts and Measures of Amount.Helen Morris Cartwright - 1975 - Noûs 9 (2):143-164.
  27. Theory Versions Instead of Articulations of a Paradigm.Ruey-lin Chen - 2000 - Studies in History and Philosophy of Science Part A 31 (3):449-471.
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  28. Sets of Probability Distributions, Independence, and Convexity.Fabio G. Cozman - 2012 - Synthese 186 (2):577-600.
    This paper analyzes concepts of independence and assumptions of convexity in the theory of sets of probability distributions. The starting point is Kyburg and Pittarelli’s discussion of “convex Bayesianism” (in particular their proposals concerning E-admissibility, independence, and convexity). The paper offers an organized review of the literature on independence for sets of probability distributions; new results on graphoid properties and on the justification of “strong independence” (using exchangeability) are presented. Finally, the connection between Kyburg and Pittarelli’s results and recent developments (...)
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  29. Alternative Formulations of the Theory of Evidence Based on Basic Plausibility and Commonality Assignments.Fabio Cuzzolin - 2008 - In Tu-Bao Ho & Zhi-Hua Zhou (eds.), Pricai 2008: Trends in Artificial Intelligence. Springer. pp. 91--102.
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  30. Probability Logic and $\Scr{F}$.A. I. Dale - 1976 - Philosophy of Science 43 (2):254-.
    In order that a degree-of-belief function be coherent it is necessary and sufficient that it satisfy the axioms of probability theory. This theorem relies heavily for its proof on the two-valued sentential calculus, which emerges as a limiting case of a continuous scale of truth-values. In this "continuum of certainty" a theorem analogous to that instanced above is proved.
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  31. Probability Logic and \Scrf.A. I. Dale - 1976 - Philosophy of Science 43 (2):254-265.
  32. Understanding Probability.Justin Dallmann - 2016 - Metascience 25 (2):331-333.
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  33. Logic and Probability in Physics.C. G. Darwin - 1939 - Philosophy of Science 6 (1):48-64.
  34. Philosophical Lectures on Probability. Collected, Edited and Annotated by Alberto Mura.Bruno De Finetti - 2008 - Springer.
    The book contains the transcription of a course on the foundations of probability given by the Italian mathematician Bruno de Finetti in 1979 at the a oeNational Institute of Advanced Mathematicsa in Rome.
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  35. The Logic of Probability.Bruno de Finetti - 1995 - Philosophical Studies 77 (1):181-190.
  36. Maximum Likelihood Unidimensional Unfolding in a Probabilistic Model Without Parametric Assumptions.G. De Soete, H. Feger & K. C. Klauer - 1989 - In Geert de Soete, Hubert Feger & Karl C. Klauer (eds.), New Developments in Psychological Choice Modeling. Distributors for the United States and Canada, Elsevier Science.
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  37. Logic and Probability.Lorenz Demey, Barteld Kooi & Joshua Sack - 2014 - In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy.
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  38. A Probabilistic Explanation for the Size-Effect in Crystal Plasticity.P. M. Derlet & R. Maaß - 2015 - Philosophical Magazine 95 (16-18):1829-1844.
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  39. A Study of Probabilities and Belief Functions Under Conflicting Evidence: Comparisons and New Methods.Mary Deutsch-McLeish - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 41--49.
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  40. The Qualitative Paradox of Non-Conglomerability.Nicholas DiBella - forthcoming - Synthese:1-30.
    A probability function is non-conglomerable just in case there is some proposition E and partition \ of the space of possible outcomes such that the probability of E conditional on any member of \ is bounded by two values yet the unconditional probability of E is not bounded by those values. The paradox of non-conglomerability is the counterintuitive—and controversial—claim that a rational agent’s subjective probability function can be non-conglomerable. In this paper, I present a qualitative analogue of the paradox. I (...)
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  41. Basic Beliefs, Coherence, and Bootstrap Confirmation.Igor Douven - 2005 - In Rene van Woudenberg, Sabine Roeser & Ron Rood (eds.), Basic Belief and Basic Knowledge. Ontos-Verlag. pp. 4--57.
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  42. The Likelihood of Knowledge.Fred Dretske - 1989 - Review of Metaphysics 42 (3):632-633.
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  43. Probability and Logic.Kenny Easwaran - 2014 - Philosophy Compass 9 (12):876-883.
    Probability and logic are two branches of mathematics that have important philosophical applications. This article discusses several areas of intersection between them. Several involve the role for probability in giving semantics for logic or the role of logic in governing assignments of probability. Some involve probability over non-classical logic or self-referential sentences.
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  44. Green and Grue Causal Variables.Frederick Eberhardt - 2016 - Synthese 193 (4).
    The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no “correct” set of causal variables. I extend Spirtes’ result to the general framework of linear structural equation models and then explore to what (...)
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  45. Probabilistic Causality and the Question of Transitivity.Ellery Eells & Elliott Sober - 1983 - Philosophy of Science 50 (1):35-57.
    After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.
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  46. E-Capacities and the Ellsberg Paradox.Jürgen Eichberger & David Kelsey - 1999 - Theory and Decision 46 (2):107-138.
    Ellsberg's (1961) famous paradox shows that decision-makers give events with ‘known’ probabilities a higher weight in their outcome evaluation. In the same article, Ellsberg suggests a preference representation which has intuitive appeal but lacks an axiomatic foundation. Schmeidler (1989) and Gilboa (1987) provide an axiomatisation for expected utility with non-additive probabilities. This paper introduces E-capacities as a representation of beliefs which incorporates objective information about the probability of events. It can be shown that the Choquet integral of an E-capacity is (...)
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  47. Confirmation, Paradox, and Logic.Leif Eriksen - 1989 - Philosophy of Science 56 (4):681-687.
    Paul Horwich has formulated a paradox which he believes to be even more virulent than the related Hempel paradox. I show that Horwich's paradox, as orginally formulated, has a purely logical solution, hence that it has no bearing on the theory of confirmation. On the other hand, it illuminates some undesirable traits of classical predicate logic. A revised formulation of the paradox is then dealt with in a way that implies a modest revision of Nicod's criterion.
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  48. Causal Knowledge: What Can Psychology Teach Philosophers.Evan Fales & Edward A. Wasserman - 1992 - Journal of Mind and Behavior 13 (1):1-28.
    Theories of how organisms learn about cause-effect relations have a history dating back at least to the associationist/mechanistic hypothesis of David Hume. Some contemporary theories of causal learning are descendants of Hume's mechanistic models of conditioning, but others impute principled, rule-based reasoning. Since even primitive animals are conditionable, it is clear that there are built-in mechanical algorithms that respond to cause/effect relations. The evidence suggests that humans retain the use of such algorithms, which are surely adaptive when causal judgments must (...)
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  49. Constructing Variables That Support Causal Inference.Stephen E. Fancsali - unknown
  50. Probability and Explanation.James H. Fetzer - 1981 - Synthese 48 (3):371 - 408.
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