Results for 'Bayesian epistemology'

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  1. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the (...)
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  2. Bayesian Epistemology.William Talbott - 2006 - Stanford Encyclopedia of Philosophy.
    Bayesian epistemology’ became an epistemological movement in the 20th century, though its two main features can be traced back to the eponymous Reverend Thomas Bayes (c. 1701-61). Those two features are: (1) the introduction of a formal apparatus for inductive logic; (2) the introduction of a pragmatic self-defeat test (as illustrated by Dutch Book Arguments) for epistemic rationality as a way of extending the justification of the laws of deductive logic to include a justification for the laws of (...)
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  3. Bayesian Epistemology.Stephan Hartmann & Jan Sprenger - 2010 - In Duncan Pritchard & Sven Bernecker (eds.), The Routledge Companion to Epistemology. London: Routledge. pp. 609-620.
    Bayesian epistemology addresses epistemological problems with the help of the mathematical theory of probability. It turns out that the probability calculus is especially suited to represent degrees of belief (credences) and to deal with questions of belief change, confirmation, evidence, justification, and coherence. Compared to the informal discussions in traditional epistemology, Bayesian epis- temology allows for a more precise and fine-grained analysis which takes the gradual aspects of these central epistemological notions into account. Bayesian (...) therefore complements traditional epistemology; it does not re- place it or aim at replacing it. (shrink)
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  4. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 2010 - In DancyJ (ed.), A Companion to Epistemology. Blackwell.
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a (...)
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  5. Bayesian Epistemology and Epistemic Conditionals: On the Status of the Export-Import Laws.Horacio Arló-Costa - 2001 - Journal of Philosophy 98 (11):555-593.
    Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
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  6. From Bayesian Epistemology to Inductive Logic.Jon Williamson - 2013 - Journal of Applied Logic 11 (4):468-486.
    Inductive logic admits a variety of semantics (Haenni et al., 2011, Part 1). This paper develops semantics based on the norms of Bayesian epistemology (Williamson, 2010, Chapter 7). §1 introduces the semantics and then, in §2, the paper explores methods for drawing inferences in the resulting logic and compares the methods of this paper with the methods of Barnett and Paris (2008). §3 then evaluates this Bayesian inductive logic in the light of four traditional critiques of inductive (...)
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    Bayesian Epistemology and Epistemic Conditionals: On the Status of the Export-Import Laws.Horacio Arló-Costa - 2001 - Journal of Philosophy 98 (11):555-593.
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  8.  69
    Bayesian Epistemology and Having Evidence.Jeffrey Dunn - 2010 - Dissertation, University of Massachusetts, Amherst
    Bayesian Epistemology is a general framework for thinking about agents who have beliefs that come in degrees. Theories in this framework give accounts of rational belief and rational belief change, which share two key features: (i) rational belief states are represented with probability functions, and (ii) rational belief change results from the acquisition of evidence. This dissertation focuses specifically on the second feature. I pose the Evidence Question: What is it to have evidence? Before addressing this question we (...)
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  9.  63
    Connecting Applied and Theoretical Bayesian Epistemology: Data Relevance, Pragmatics, and the Legal Case of Sally Clark.Matthew J. Barker - 2017 - Journal of Applied Philosophy 34 (2):242-262.
    In this article applied and theoretical epistemologies benefit each other in a study of the British legal case of R. vs. Clark. Clark's first infant died at 11 weeks of age, in December 1996. About a year later, Clark had a second child. After that child died at eight weeks of age, Clark was tried for murdering both infants. Statisticians and philosophers have disputed how to apply Bayesian analyses to this case, and thereby arrived at different judgments about it. (...)
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  10. Confirmational Holism and Bayesian Epistemology.David Christensen - 1992 - Philosophy of Science 59 (4):540-557.
    Much contemporary epistemology is informed by a kind of confirmational holism, and a consequent rejection of the assumption that all confirmation rests on experiential certainties. Another prominent theme is that belief comes in degrees, and that rationality requires apportioning one's degrees of belief reasonably. Bayesian confirmation models based on Jeffrey Conditionalization attempt to bring together these two appealing strands. I argue, however, that these models cannot account for a certain aspect of confirmation that would be accounted for in (...)
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  11.  66
    Two Principles of Bayesian Epistemology.William Talbott - 1991 - Philosophical Studies 62 (2):135-150.
  12. Evidential Probability and Objective Bayesian Epistemology.Gregory Wheeler & Jon Williamson - 2011 - In Prasanta S. Bandyopadhyay & Malcolm Forster (eds.), Handbook of the Philosophy of Science, Vol. 7: Philosophy of Statistics. Elsevier.
    In this chapter we draw connections between two seemingly opposing approaches to probability and statistics: evidential probability on the one hand and objective Bayesian epistemology on the other.
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  13.  12
    Bayesian Epistemology.Jürgen Landes - 2022 - Kriterion – Journal of Philosophy 36 (1):1-7.
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  14.  13
    Bayesian Epistemology.Ellery Eells - 1994 - ProtoSociology 6:33-60.
    This paper distinguishes between "descriptive" and "normative" conceptions of Bayesian principles of rationality, both in the context of inference and in the context of decision. I emphasize an idea according to which, "You have to work with what you have to work with" - that is, that rationality is a relation among old beliefs, new information, and new beliefs and among beliefs, desires, preferences, and choices. According to this conception of rationality, one's current beliefs and desires are not themselves (...)
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  15. Evidentialism and Conservatism in Bayesian Epistemology.Wolfgang Schwarz - ms.
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  16.  34
    Bayesian Epistemology as a Case Study in Unhelpful Idealization.Mark Lance - 2000 - In N. Shanks & R. Gardner (eds.), Logic, Probability and Science. Atlanta: Rodopi. pp. 112.
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  17.  86
    Bayesian Epistemology.Erik J. Olsson - 2018 - In Sven Ove Hansson & Vincent Hendricks (eds.), Introduction to Formal Philosophy. Springer. pp. 431-442.
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  18. Bayesian Epistemology: Probabilistic Confirmation and Rational Decision.Ellery Eells - 1995 - ProtoSociology 6.
     
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  19. Reasons for (Prior) Belief in Bayesian Epistemology.Franz Dietrich & Christian List - 2013 - Synthese 190 (5):781-786.
    Bayesian epistemology tells us with great precision how we should move from prior to posterior beliefs in light of new evidence or information, but says little about where our prior beliefs come from. It offers few resources to describe some prior beliefs as rational or well-justified, and others as irrational or unreasonable. A different strand of epistemology takes the central epistemological question to be not how to change one’s beliefs in light of new evidence, but what reasons (...)
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  20. Bayesian Epistemology.Robert Williams - manuscript
    Synthese 156 (3) (2007). Special issue ed. with Luc Bovens. With contributions by Max Albert, Branden Fitelson, Dennis Dieks, Igor Douven and Wouter Meijs, Alan Hájek, Colin Howson, James Joyce, and Patrick Suppes.
     
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  21. Problems for Bayesian Epistemology.John L. Pollock - unknown
    In the past, few mainstream epistemologists have endorsed Bayesian epistemology, feeling that it fails to capture the complex structure of epistemic cognition. The defenders of Bayesian epistemology have tended to be probability theorists rather than epistemologists, and I have always suspected they were more attracted by its mathematical elegance than its epistemological realism. But recently Bayesian epistemology has gained a following among younger mainstream epistemologists. I think it is time to rehearse some of the (...)
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  22. Reasons as Causes in Bayesian Epistemology.Clark Glymour & David Danks - 2007 - Journal of Philosophy 104 (9):464-474.
    In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between (...)
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  23.  25
    Problems of Precision in Fuzzy Theories of Vagueness and Bayesian Epistemology.Nicholas J. J. Smith - 2019 - In Richard Dietz (ed.), Vagueness and Rationality in Language Use and Cognition. Springer Verlag. pp. 31-48.
    A common objection to theories of vagueness based on fuzzy logics centres on the idea that assigning a single numerical degree of truth -- a real number between 0 and 1 -- to each vague statement is excessively precise. A common objection to Bayesian epistemology centres on the idea that assigning a single numerical degree of belief -- a real number between 0 and 1 -- to each proposition is excessively precise. In this paper I explore possible parallels (...)
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  24. Book Review: Luc Bovens and Stephan Hartmann "Bayesian Epistemology". [REVIEW]Erik J. Olsson - 2005 - Studia Logica 81 (2):289-292.
    Book Review of Luc Bovens and Stephan Hartmann *Bayesian Epistemology* by Erik J. Olsson.
     
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  25. Book Review: Luc Bovens and Stephan Hartmann "Bayesian Epistemology". [REVIEW]Branden Fitelson - 2005 - Mind 114 (454):394-400.
    Book Review of Luc Bovens and Stephan Hartmann *Bayesian Epistemology* by Branden Fitelson.
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  26. Induction, Probability, and Bayesian Epistemology.Roberto Festa - 2003 - In Leila Haaparanta & Ilkka Niiniluoto (eds.), Analitical Philosophy in Finland. Rodopi. pp. 251-284.
    Finland is internationally known as one of the leading centers of twentieth century analytic philosophy. This volume offers for the first time an overall survey of the Finnish analytic school. The rise of this trend is illustrated by original articles of Edward Westermarck, Eino Kaila, Georg Henrik von Wright, and Jaakko Hintikka. Contributions of Finnish philosophers are then systematically discussed in the fields of logic, philosophy of language, philosophy of science, history of philosophy, ethics and social philosophy. Metaphilosophical reflections on (...)
     
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  27. Induction, Probability, and Bayesian Epistemology.Roberto Festa - 2003 - Poznan Studies in the Philosophy of the Sciences and the Humanities 80:251-284.
     
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  28.  11
    Artificial Intelligence Methods for a Bayesian Epistemology‐Powered Evidence Evaluation.Francesco De Pretis, Jürgen Landes & William Peden - 2021 - Journal of Evaluation in Clinical Practice 27 (3):504-512.
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  29.  27
    Assessing the Credence of Bayesian Epistemology: Richard Pettigrew's: Accuracy and the Laws of Credence. Oxford University Press, 2016, 256 Pp, $74.00 HB.Erik J. Olsson - 2017 - Metascience 26 (2):245-247.
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  30.  11
    Review of Bayesian Epistemology.Erik J. Olsson - 2005 - Studia Logica 81:443-446.
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  31.  77
    Special Issue of Synthese on Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2007 - Synthese 156 (3):403-403.
    The papers in this collection were presented at a workshop on Bayesian Epistemology at the 26th International Wittgenstein Symposium in Kirchberg, Austria (August 4–7, 2003), at a workshop on Philosophy and Probability at the conference GAP5 in Bielefeld, Germany (September 20–22, 2003), at a workshop on Bayesian Epistemology at the Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science in London, UK (June 28, 2004), or at the seminar of the (...)
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  32. My Way or Her Way: A Conundrum in Bayesian Epistemology of Disagreement.Tomoji Shogenji - manuscript
    The proportional weight view in epistemology of disagreement generalizes the equal weight view and proposes that we assign to judgments of different people weights that are proportional to their epistemic qualifications. It is shown that if the resulting degrees of confidence are to constitute a probability function, they must be the weighted arithmetic means of individual degrees of confidence, while if the resulting degrees of confidence are to obey the Bayesian rule of conditionalization, they must be the weighted (...)
     
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  33.  36
    Frá skoðunum til trúnaðar og aftur til baka: Yfirlit um bayesíska þekkingarfræði [English title: "From Belief to Credence and Back Again: An Overview of Bayesian Epistemology"].Finnur Dellsén - 2017 - Hugur 28:146-162.
    English abstract: This paper discusses the delicate relationship between traditional epistemology and the increasingly influential probabilistic (or ‘Bayesian’) approach to epistemology. The paper introduces some of the key ideas of probabilistic epistemology, including credences or degrees of belief, Bayes’ theorem, conditionalization, and the Dutch Book argument. The tension between traditional and probabilistic epistemology is brought out by considering the lottery and preface paradoxes as they relate to rational (binary) belief and credence respectively. It is then (...)
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  34.  39
    Time-Slice Epistemology for Bayesians.Lisa Cassell - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    Recently, some have challenged the idea that there are genuine norms of diachronic rationality. Part of this challenge has involved offering replacements for diachronic principles. Skeptics about diachronic rationality believe that we can provide an error theory for it by appealing to synchronic updating rules that, over time, mimic the behavior of diachronic norms. In this paper, I argue that the most promising attempts to develop this position within the Bayesian framework are unsuccessful. I sketch a new synchronic surrogate (...)
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  35.  44
    Luc Bovens and Stephan Hartmann, Bayesian Epistemology Oxford University Press, 2004, Pp. IX+ 159.Isbn 0-19-926975-0 (Hardback), Isbn 0-19-927040-6 (Paperback). [REVIEW]Toinoji Shogenji - 2006 - Theoria 72 (2):166-171.
  36.  28
    Coherence and Reliability: Studies in Bayesian Epistemology.Stefan Schubert - unknown
    In this thesis the connection between coherence and reliability is investigated. The question may be phrased as follows: does the fact that a set of testimonies is coherent imply that the witnesses who have reported these testimonies are reliable? The same question may also be expressed in terms of beliefs: does the fact that a set of beliefs is coherent imply that the beliefs were reliably produced? Traditionally, coherence theorists have thought that coherence is connected to truth, but in this (...)
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  37. Giving Up Judgment Empiricism: The Bayesian Epistemology of Bertrand Russell and Grover Maxwell.James Hawthorne - 1989 - In C. Wade Savage & C. Anthony Anderson (eds.), ReReading Russell: Bertrand Russell's Metaphysics and Epistemology; Minnesota Studies in the Philosophy of Science, Volume 12. University of Minnesota Press.
    This essay is an attempt to gain better insight into Russell's positive account of inductive inference. I contend that Russell's postulates play only a supporting role in his overall account. At the center of Russell's positive view is a probabilistic, Bayesian model of inductive inference. Indeed, Russell and Maxwell actually held very similar Bayesian views. But the Bayesian component of Russell's view in Human Knowledge is sparse and easily overlooked. Maxwell was not aware of it when he (...)
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  38. The Best is the Enemy of the Good: Bayesian Epistemology as a Case Study in Unhelpful Idealization Commentary.L. Nowak - 2000 - Poznan Studies in the Philosophy of the Sciences and the Humanities 71:112-135.
  39. The Best is the Enemy of the Good: Bayesian Epistemology as a Case Study in Unhelpful Idealization.M. N. Lance - 2000 - Poznan Studies in the Philosophy of the Sciences and the Humanities 71:112-135.
     
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  40. Troubles for Bayesian Formal Epistemology.Terry Horgan - 2017 - Res Philosophica 94 (2):233-255.
    I raise skeptical doubts about the prospects of Bayesian formal epistemology for providing an adequate general normative model of epistemic rationality. The notion of credence, I argue, embodies a very dubious psychological myth, viz., that for virtually any proposition p that one can entertain and understand, one has some quantitatively precise, 0-to-1 ratio-scale, doxastic attitude toward p. The concept of credence faces further serious problems as well—different ones depending on whether credence 1 is construed as full belief or (...)
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  41.  59
    Troubles for Bayesian Formal Epistemology?Jonah N. Schupbach - 2017 - Res Philosophica 95 (1):189-197.
    This paper responds to Terry Horgan’s recent critique of Bayesian formal epistemology. I argue that each of Horgan’s criticisms misses its mark when Bayesianism is viewed as putting forward an inductive logic of confidences. Along the way, I explore the nature, scope, and limits of a defensible brand of Bayesianism.
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  42. Calibration and the Epistemological Role of Bayesian Conditionalization.Marc Lange - 1999 - Journal of Philosophy 96 (6):294-324.
  43. Objective Bayesian Calibration and the Problem of Non-Convex Evidence.Gregory Wheeler - 2012 - British Journal for the Philosophy of Science 63 (4):841-850.
    Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence. One role of the calibration norm is to ensure that evidence works to constrain a convex set of probability functions. This essay brings into focus a problem for Williamson's theory when qualitative evidence specifies non-convex constraints.
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  44. Bayesian Updating When What You Learn Might Be False.Richard Pettigrew - forthcoming - Erkenntnis:1-16.
    Michael Rescorla (2020) has recently pointed out that the standard arguments for Bayesian Conditionalization assume that whenever you take yourself to learn something with certainty, it's true. Most people would reject this assumption. In response, Rescorla offers an improved Dutch Book argument for Bayesian Conditionalization that does not make this assumption. My purpose in this paper is two-fold. First, I want to illuminate Rescorla's new argument by giving a very general Dutch Book argument that applies to many cases (...)
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  45.  44
    Bayesian Statistics and Popper's Epistemology.M. Hammerton - 1968 - Mind 77 (305):109-112.
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  46. Epistemology without guidance.Nick Hughes - 2021 - Philosophical Studies 179 (1):163-196.
    Epistemologists often appeal to the idea that a normative theory must provide useful, usable, guidance to argue for one normative epistemology over another. I argue that this is a mistake. Guidance considerations have no role to play in theory choice in epistemology. I show how this has implications for debates about the possibility and scope of epistemic dilemmas, the legitimacy of idealisation in Bayesian epistemology, uniqueness versus permissivism, sharp versus mushy credences, and internalism versus externalism.
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  47. A Bayesian Analysis of Debunking Arguments in Ethics.Shang Long Yeo - 2021 - Philosophical Studies 179 (5):1673-1692.
    Debunking arguments in ethics contend that our moral beliefs have dubious evolutionary, cultural, or psychological origins—hence concluding that we should doubt such beliefs. Debates about debunking are often couched in coarse-grained terms—about whether our moral beliefs are justified or not, for instance. In this paper, I propose a more detailed Bayesian analysis of debunking arguments, which proceeds in the fine-grained framework of rational confidence. Such analysis promises several payoffs: it highlights how debunking arguments don’t affect all agents, but rather (...)
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  48. Bayesian Norms and Non-Ideal Agents.Julia Staffel - forthcoming - In Maria Lasonen-Aarnio & Clayton M. Littlejohn (eds.), Routledge Handbook of the Philosophy Evidence. Routledge.
    Bayesian epistemology provides a popular and powerful framework for modeling rational norms on credences, including how rational agents should respond to evidence. The framework is built on the assumption that ideally rational agents have credences, or degrees of belief, that are representable by numbers that obey the axioms of probability. From there, further constraints are proposed regarding which credence assignments are rationally permissible, and how rational agents’ credences should change upon learning new evidence. While the details are hotly (...)
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  49. The Bayesian Explanation of Transmission Failure.Geoff Pynn - 2013 - Synthese 190 (9):1519-1531.
    Even if our justified beliefs are closed under known entailment, there may still be instances of transmission failure. Transmission failure occurs when P entails Q, but a subject cannot acquire a justified belief that Q by deducing it from P. Paradigm cases of transmission failure involve inferences from mundane beliefs (e.g., that the wall in front of you is red) to the denials of skeptical hypotheses relative to those beliefs (e.g., that the wall in front of you is not white (...)
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  50. The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science.Matthias Unterhuber & Gerhard Schurz - 2013 - Synthese 190 (8):1407-1435.
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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