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  1. A. R. A. (1957). Probability in Logic. Review of Metaphysics 11 (2):348-348.
  2. R. A. A. (1956). Risk and Gambling: The Study of Subjective Probability. [REVIEW] Review of Metaphysics 10 (1):174-174.
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  3. Ernest W. Adams & Roger D. Rosenkrantz (1980). Applying the Jeffrey Decision Model to Rational Betting and Information Acquisition. Theory and Decision 12 (1):1-20.
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  4. Edward H. Allen (1976). Negative Probabilities and the Uses of Signed Probability Theory. Philosophy of Science 43 (1):53-70.
    The use of negative probabilities is discussed for certain problems in which a stochastic process approach is indicated. An extension of probability theory to include signed (negative and positive) probabilities is outlined and both philosophical and axiomatic examinations of negative probabilities are presented. Finally, a class of applications illustrates the use and implications of signed probability theory.
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  5. R. Amer, S. Bourdet-Loubère, I. Brocas, R. G. Brody, M. H. Broihanne, D. Cardona-Coll, H. W. Chesson, T. Clausing, P. Corcho & J. M. Coulter (2003). Theory and Decison. Theory and Decision 54 (376).
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  6. Norman H. Anderson (1960). Effect of First-Order Conditional Probability in a Two-Choice Learning Situation. Journal of Experimental Psychology 59 (2):73.
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  7. Norman H. Anderson & James C. Shanteau (1970). Information Integration in Risky Decision Making. Journal of Experimental Psychology 84 (3):441.
    Applied a theory of information integration to decision making with probabilistic events. 10 undergraduates judged the subjective worth of duplex bets that included independent gain and lose components. The worth of each component was assumed to be the product of a subjective weight that reflected the probability of winning or losing, and the subjective worth of the money to be won or lost. The total worth of the bet was the sum of the worths of the 2 components. Thus, each (...)
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  8. David Atkinson & Jeanne Peijnenburg (2006). Probability All the Way Up. Synthese 153 (2):187 - 197.
    Richard Jeffrey’s radical probabilism (‘probability all the way down’) is augmented by the claim that probability cannot be turned into certainty, except by data that logically exclude all alternatives. Once we start being uncertain, no amount of updating will free us from the treadmill of uncertainty. This claim is cast first in objectivist and then in subjectivist terms.
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  9. David Atkinson & Jeanne Peijnenburg (2006). Probability All the Way Up. Synthese 153 (2):187 - 197.
    Richard Jeffrey's radical probabilism ('probability all the way down') is augmented by the claim that probability cannot be turned into certainty, except by data that logically exclude all alternatives. Once we start being uncertain, no amount of updating will free us from the treadmill of uncertainty. This claim is cast first in objectivist and then in subjectivist terms.
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  10. David Atkinson & Jeanne Peijnenburg (1999). Probability as a Theory Dependent Concept. Synthese 118 (3):307-328.
    It is argued that probability should be defined implicitly by the distributions of possible measurement values characteristic of a theory. These distributions are tested by, but not defined in terms of, relative frequencies of occurrences of events of a specified kind. The adoption of an a priori probability in an empirical investigation constitutes part of the formulation of a theory. In particular, an assumption of equiprobability in a given situation is merely one hypothesis inter alia, which can be tested, like (...)
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  11. James Augustus Atkinson (1897). Betting and Gambling, a Paper.
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  12. Fred Attneave (1953). Psychological Probability as a Function of Experienced Frequency. Journal of Experimental Psychology 46 (2):81.
  13. Alfred Jules Ayer (1961). On the Probability of Particular Events. Revue Internationale de Philosophie 15 (58):366-75.
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  14. Michael Bacharach (1987). A Theory of Rational Decision in Games. Erkenntnis 27 (1):17 - 55.
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  15. Michael Bacharach & Susan Hurley (eds.) (1991). Essays in the Foundations of Decision Theory. Basil Blackwell, Inc..
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  16. Beth Baker (1999). The Greening of Utilities. BioScience 49 (8):612.
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  17. Beth Baker (1999). The Greening of Utilities Biologists Are Making a Difference at Electric Utilities Across the United States. BioScience 49 (8):612-616.
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  18. James F. Baldwin (1991). Towards a General Theory of Evidential Reasoning. In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. 359--369.
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  19. Maya Bar-Hillel (1989). How to Solve Probability Teasers. Philosophy of Science 56 (2):348-358.
    Recently, Nathan (1986) criticized Bar-Hillel and Falk's (1982) analysis of some classical probability puzzles on the grounds that they wrongheadedly applied mathematics to the solving of problems suffering from "ambiguous informalities". Nathan's prescription for solving such problems boils down to assuring in advance that they are uniquely and formally soluble--though he says little about how this is to be done. Unfortunately, in real life problems seldom show concern as to whether their naturally occurring formulation is or is not ambiguous, does (...)
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  20. Stephen J. Barker (1998). Predetermination and Tense Probabilism. Analysis 58 (4):290–296.
  21. G. A. Barnard (1972). Two Points in the Theory of Statistical Inference. British Journal for the Philosophy of Science 23 (4):329-331.
  22. Samuel Barnett (1921). Philosophy and Probability. Philosophical Review 30 (6):585-601.
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  23. Walter Arthur Bass (1953). Some Difficulties in the Theory of Probability. Dissertation, University of Virginia
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  24. Lee R. Beach (1968). Probability Magnitudes and Conservative Revision of Subjective Probabilities. Journal of Experimental Psychology 77 (1):57.
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  25. Lee R. Beach & James A. Wise (1969). Subjective Probability and Decision Strategy. Journal of Experimental Psychology 79 (1p1):133.
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  26. Joseph Berkovitz (2012). The World According to de Finetti: On de Finetti's Theory of Probability and Its Application to Quantum Mechanics. In Yemima Ben-Menahem & Meir Hemmo (eds.), Probability in Physics. Springer. 249--280.
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  27. Marilyn Berman, Malcolm P. Fraser & John Theios (1970). Learning a General Maximum Likelihood Decision Strategy. Journal of Experimental Psychology 86 (3):393.
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  28. Betting (1895). Betting and Gambling, a Paper by a Student.
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  29. Rabi Bhattacharya & Edward C. Waymire (forthcoming). A Basic Course in Probability Theory. Analysis.
    The book develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. With this goal in mind, the pace is lively, yet thorough. Basic notions of independence and conditional expectation are introduced relatively early on in the text, while conditional expectation is illustrated in detail in the context of martingales, Markov property and strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two highlights. The historic role of size-biasing (...)
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  30. Cristina Bicchieri & Maria Luisa Dalla Chiara (1992). Knowledge, Belief and Strategic Interaction. Monograph Collection (Matt - Pseudo).
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  31. Cristina Bicchieri & Oliver Schulte (1996). Common Reasoning About Admissibility. Erkenntnis 45 (2-3):299 - 325.
    We analyze common reasoning about admissibility in the strategic and extensive form of a game. We define a notion of sequential proper admissibility in the extensive form, and show that, in finite extensive games with perfect recall, the strategies that are consistent with common reasoning about sequential proper admissibility in the extensive form are exactly those that are consistent with common reasoning about admissibility in the strategic form representation of the game. Thus in such games the solution given by common (...)
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  32. Klinton Bicknell & Roger Levy (2010). Rational Eye Movements in Reading Combining Uncertainty About Previous Words with Contextual Probability. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  33. Glenys Bishop (2013). Statistical Analyses. In Katrina Hutchison & Fiona Jenkins (eds.), Women in Philosophy: What Needs to Change? Oup Usa. 253.
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  34. Michael Bleaney & Steven J. Humphrey (2006). An Experimental Test of Generalized Ambiguity Aversion Using Lottery Pricing Tasks. Theory and Decision 60 (2-3):257-282.
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  35. Richard Frederick L. Blunt (1902). Betting and Gambling, a Paper.
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  36. Giacomo Bonanno & Klaus Nehring (1999). How to Make Sense of the Common Prior Assumption Under Incomplete Information. International Journal of Game Theory 28 (3):409-434.
    The Common Prior Assumption (CPA) plays an important role in game theory and the economics of information. It is the basic assumption behind decision-theoretic justifications of equilibrium reasoning in games (Aumann, 1987, Aumann and Brandenburger, 1995) and no-trade results with asymmetric information (Milgrom and Stokey, 1982). Recently several authors (Dekel and Gul, 1997, Gul, 1996, Lipman, 1995) have questioned whether the CPA is meaningful in situations of incomplete information, where there is no ex ante stage and where the primitives of (...)
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  37. Karl Borch (1975). Probabilities of Probabilities. Theory and Decision 6 (2):155-159.
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  38. Karl Borch (1970). Specification of Objectives in Decision Problems. Theory and Decision 1 (1):5-21.
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  39. Nicolas Bouleau, What Are the Philosophical Probabilities ?
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  40. Arthur Boutwood, Shadworth H. Hodgson, H. W. Carr & J. Lindsay (1901). The Philosophy of Probability [with Discussion]. Proceedings of the Aristotelian Society 2:74 - 104.
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  41. Richard W. Bradley (1997). The Representation of Beliefs and Desires Within Decision Theory. Dissertation, The University of Chicago
    This dissertation interprets the lack of uniqueness in probability representations of agents' degrees of belief in the decision theory of Richard Jeffrey as a formal statement of an important epistemological problem: the underdetermination of our attributions of belief and desire to agents by the evidence of their observed behaviour. A solution is pursued through investigation of agents' attitudes to information of a conditional nature. ;As a first step, Jeffrey's theory is extended to agents' conditional attitudes of belief and desire by (...)
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  42. R. B. Braithwaite (1941). JEFFREYS, H. -Theory of Probability. [REVIEW] Mind 50:198.
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  43. Richard G. Brody, John M. Coulter, Alireza Daneshfar, Auditor Probability Judgments, Discounting Unspecified Possibilities, Paula Corcho, José Luis Ferreira & Generalized Externality Games (2003). Theory and Decison. Theory and Decision 54:375-376.
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  44. Haines Brown (2014). A Process Ontology. Axiomathes 24 (3):291-312.
    The paper assumes that to be of practical interest process must be understood as physical action that takes place in the world rather than being an idea in the mind. It argues that if an ontology of process is to accommodate actuality, it must be represented in terms of relative probabilities. Folk physics cannot accommodate this, and so the paper appeals to scientific culture because it is an emergent knowledge of the world derived from action in it. Process is represented (...)
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  45. Anthony Brueckner & Alex Bundy (2012). On “Epistemic Permissiveness”. Synthese 188 (2):165-177.
    In "Epistemic Permissiveness", Roger White presents several arguments against Extreme Permissivism, the view that there are possible cases where, given one's total evidence, it would be rational to either believe P, or to believe ~P. In this paper, we carefully reconstruct White's arguments and then argue that they do not succeed.
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  46. Franciszek Bujak (1975). A Subjective Appraisal of the Probability of Happenings Related by Children From 8 to 15 Years of Age. Roczniki Filozoficzne 23 (4):120.
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  47. Michael Caie (2014). Calibration and Probabilism. Ergo 1.
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  48. Bruno Carbonaro & Federica Vitale (2013). The Raven Paradox Revisited in Terms of Random Variables. Erkenntnis 78 (4):763-795.
    The discussion about the Raven Paradox is ever-renewing: after nearly 70 years, many authors propose from time to time new solutions, and many authors state that these solutions are unsatisfactory. It is worthy to be carefully noted that though most arguments in favor or against the paradox are based on the notion of “probability” and on the application of Bayes’ law, not one of them makes use of the Kolmogorov axiomatic theory of probability and on the subsequent notion of “random (...)
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  49. Rudolf Carnap (1963). The Logical Foundations of Probability. Journal of Philosophy 60 (13):362-364.
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  50. Rudolf Carnap (1947). Probability as a Guide in Life. Journal of Philosophy 44 (6):141-148.
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