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  1. added 2020-05-29
    It All Adds Up: The Dynamic Coherence of Radical Probabilism.S. L. Zabell - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S98-S103.
  2. added 2020-05-22
    Belief Revision Generalized: A Joint Characterization of Bayes's and Jeffrey's Rules.Franz Dietrich, Christian List & Richard Bradley - 2016 - Journal of Economic Theory 162:352-371.
    We present a general framework for representing belief-revision rules and use it to characterize Bayes's rule as a classical example and Jeffrey's rule as a non-classical one. In Jeffrey's rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes's rule, but a new assignment of probabilities to some events. Despite their differences, Bayes's and Jeffrey's rules can be characterized in terms of the same axioms: "responsiveness", which requires that revised beliefs (...)
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  3. added 2020-05-20
    Trung tâm ISR có bài ra mừng 130 năm Ngày sinh Chủ tịch Hồ Chí Minh.Hồ Mạnh Toàn - 2020 - ISR Phenikaa 2020 (5):1-3.
    Bài mới xuất bản vào ngày 19-5-2020 với tác giả liên lạc là NCS Nguyễn Minh Hoàng, cán bộ nghiên cứu của Trung tâm ISR, trình bày tiếp cận thống kê Bayesian cho việc nghiên cứu dữ liệu khoa học xã hội. Đây là kết quả của định hướng Nhóm nghiên cứu SDAG được nêu rõ ngay từ ngày 18-5-2019.
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  4. added 2020-05-11
    Bayesian Coherentism.Lisa Cassell - forthcoming - Synthese:1-28.
    This paper considers a problem for Bayesian epistemology and proposes a solution to it. On the traditional Bayesian framework, an agent updates her beliefs by Bayesian conditioning, a rule that tells her how to revise her beliefs whenever she gets evidence that she holds with certainty. In order to extend the framework to a wider range of cases, Jeffrey (1965) proposed a more liberal version of this rule that has Bayesian conditioning as a special case. Jeffrey conditioning is a rule (...)
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  5. added 2020-05-05
    Improving Bayesian Statistics Understanding in the Age of Big Data with the Bayesvl R Package.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Manh-Toan Ho, Manh-Tung Ho & Peter Mantello - 2020 - Software Impacts 4 (1):100016.
    The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability (...)
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  6. added 2020-05-03
    Bayesian Updating When What You Take Yourself to Learn Might Be False.Richard Pettigrew - manuscript
    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 of updating (...)
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  7. added 2020-04-24
    Accurate Updating for the Risk-Sensitive.Catrin Campbell-Moore & Bernhard Salow - forthcoming - British Journal for the Philosophy of Science:axaa006.
    Philosophers have recently attempted to justify particular belief revision procedures by arguing that they are the optimal means towards the epistemic end of accurate credences. These attempts, however, presuppose that means should be evaluated according to classical expected utility theory; and there is a long tradition maintaining that expected utility theory is too restrictive as a theory of means–end rationality, ruling out too many natural ways of taking risk into account. In this paper, we investigate what belief-revision procedures are supported (...)
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  8. added 2020-04-19
    A Theory of Bayesian Groups.Franz Dietrich - 2019 - Noûs 53 (3):708-736.
    A group is often construed as one agent with its own probabilistic beliefs (credences), which are obtained by aggregating those of the individuals, for instance through averaging. In their celebrated “Groupthink”, Russell et al. (2015) require group credences to undergo Bayesian revision whenever new information is learnt, i.e., whenever individual credences undergo Bayesian revision based on this information. To obtain a fully Bayesian group, one should often extend this requirement to non-public or even private information (learnt by not all or (...)
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  9. added 2020-03-31
    A New Solution to the Problem of Old Evidence.Stephan Hartmann - unknown
    The Problem of Old Evidence has troubled Bayesians ever since Clark Glymour first presented it in 1980. Several solutions have been proposed, but all of them have drawbacks and none of them is considered to be the definite solution. In this article, I propose a new solution which combines several old ideas with a new one. It circumvents the crucial omniscience problem in an elegant way and leads to a considerable confirmation of the hypothesis in question.
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  10. added 2020-02-18
    Bayesian Beauty.Silvia Milano - forthcoming - Erkenntnis:1-20.
    The Sleeping Beauty problem has attracted considerable attention in the literature as a paradigmatic example of how self-locating uncertainty creates problems for the Bayesian principles of Conditionalization and Reflection. Furthermore, it is also thought to raise serious issues for diachronic Dutch Book arguments. I show that, contrary to what is commonly accepted, it is possible to represent the Sleeping Beauty problem within a standard Bayesian framework. Once the problem is correctly represented, the ‘thirder’ solution satisfies standard rationality principles, vindicating why (...)
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  11. added 2020-02-07
    Impermissive Bayesianism.Christopher Meacham - 2013 - Erkenntnis 79 (Suppl 6):1185-1217.
    This paper examines the debate between permissive and impermissive forms of Bayesianism. It briefly discusses some considerations that might be offered by both sides of the debate, and then replies to some new arguments in favor of impermissivism offered by Roger White. First, it argues that White’s defense of Indifference Principles is unsuccessful. Second, it contends that White’s arguments against permissive views do not succeed.
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  12. added 2020-02-05
    Avoiding Both the Garbage-In/Garbage-Out and the Borel Paradox in Updating Probabilities Given Experimental Information.Robert F. Bordley - 2015 - Theory and Decision 79 (1):95-105.
    Bayes Rule specifies how probabilities over parameters should be updated given any kind of information. But in some cases, the kind of information provided by both simulation and physical experiments is information on how certain output parameters may change when other input parameters are changed. There are three different approaches to this problem, one of which leads to the Garbage-In/garbage-out Paradox, the second of which violates the Borel Paradox, and the third of which is a supra-Bayesian heuristic. This paper shows (...)
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  13. added 2020-01-22
    Generalized Jeffrey Conditionalization.Dirk Draheim - 2017 - Springer.
    This book provides a frequentist semantics for conditionalization on partially known events, which is given as a straightforward generalization of classical conditional probability via so-called probability testbeds. It analyzes the resulting partial conditionalization, called frequentist partial (F.P.) conditionalization, from different angles, i.e., with respect to partitions, segmentation, independence, and chaining. It turns out that F.P. conditionalization meets and generalizes Jeffrey conditionalization, i.e., from partitions to arbitrary collections of events, opening it for reassessment and a range of potential applications. A counterpart (...)
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  14. added 2020-01-05
    The Value of Biased Information.Nilanjan Das - forthcoming - British Journal for the Philosophy of Science:axaa003.
    In this essay, I cast doubt on an apparent truism: namely, that if evidence is available for gathering and use at a negligible cost, then it's always instrumentally rational for us to gather that evidence and use it for making decisions. Call this thesis Value of Information. I show that Value of Information conflicts with two other plausible theses. The first is the view that an agent's evidence can entail non-trivial propositions about the external world. The second is the view (...)
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  15. added 2019-12-16
    A Note on Deterministic Updating and van Fraassen’s Symmetry Argument for Conditionalization.Richard Pettigrew - forthcoming - Philosophical Studies:1-9.
    In a recent paper, I argue that the pragmatic and epistemic arguments for Bayesian updating are based on an unwarranted assumption, which I call Deterministic Updating, and which says that your updating plan should be deterministic. In that paper, I did not consider whether the symmetry arguments due to Hughes and van Fraassen make the same assumption (Hughes & van Fraassen 1984; van Fraassen 1987). In this note, I show that they do.
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  16. added 2019-11-25
    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 epistemology therefore complements traditional epistemology; it (...)
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  17. added 2019-11-15
    Logical Ignorance and Logical Learning.Richard Pettigrew - forthcoming - Synthese.
    According to certain normative theories in epistemology, rationality requires us to be logically omniscient. Yet this prescription clashes with our ordinary judgments of rationality. How should we resolve this tension? In this paper, I focus particularly on the logical omniscience requirement in Bayesian epistemology. Building on a key insight by Ian Hacking (1967), I develop a version of Bayesianism that permits logical ignorance. This includes an account of the synchronic norms that govern a logically ignorant individual at any given time, (...)
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  18. added 2019-11-08
    An Automatic Ockham’s Razor for Bayesians?Gordon Belot - 2019 - Erkenntnis 84 (6):1361-1367.
    It is sometimes claimed that the Bayesian framework automatically implements Ockham’s razor—that conditionalizing on data consistent with both a simple theory and a complex theory more or less inevitably favours the simpler theory. It is shown here that the automatic razor doesn’t in fact cut it for certain mundane curve-fitting problems.
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  19. added 2019-09-14
    Epistemic Diversity and Editor Decisions: A Statistical Matthew Effect.Remco Heesen & Jan-Willem Romeijn - 2019 - Philosophers' Imprint 19.
    This paper offers a new angle on the common idea that the process of science does not support epistemic diversity. Under minimal assumptions on the nature of journal editing, we prove that editorial procedures, even when impartial in themselves, disadvantage less prominent research programs. This purely statistical bias in article selection further skews existing differences in the success rate and hence attractiveness of research programs, and exacerbates the reputation difference between the programs. After a discussion of the modeling assumptions, the (...)
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  20. added 2019-06-06
    Desire-as-Belief Implies Opinionation or Indifference.Horacio Costa, John Collins & Isaac Levi - 1995 - Analysis 55 (1):2-5.
    The anti- Humean proposal of constructing desire as belief about what would be good must be abandoned on pain of triviality. Our central result shows that if an agent's belief- desire state is represented by Jeffrey's expected value theory enriched with the Desire as Belief Thesis (DAB), then, provided that three pairwise inconsistent propositions receive non- zero probability, the agent must view with indifference any proposition whose probability is greater than zero. Unlike previous results against DAB our Opinionation or Indifference (...)
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  21. added 2019-06-03
    Confirmation Based on Analogical Inference: Bayes Meets Jeffrey.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2020 - Canadian Journal of Philosophy 50 (2):174-194.
    Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (in press) idea of analyzing confirmation based on analogical inference Baysian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a (...)
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  22. added 2019-06-03
    Does Roush Show That Evidence Should Be Probable?Damien Fennell & Nancy Cartwright - 2009 - Centre for the Philosophy of Natural and Social Science, London School of Economics and Political Science.
    This paper critically analyzes Sherrilyn Roush’s definition of evidence and especially her powerful defence that in the ideal, a claim should be probable to be evidence for anything. We suggest that Roush treats not one sense of ‘evidence’ but three: relevance, leveraging and grounds for knowledge; and that different parts of her argument fare differently with respect to different senses. For relevance, we argue that probable evidence is sufficient but not necessary for Roush’s own two criteria of evidence to be (...)
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  23. added 2019-05-20
    On Being a Random Sample.David Manley - manuscript
    It is well known that de se (or ‘self-locating’) propositions complicate the standard picture of how we should respond to evidence. This has given rise to a substantial literature centered around puzzles like Sleeping Beauty, Dr. Evil, and Doomsday—and it has also sparked controversy over a style of argument that has recently been adopted by theoretical cosmologists. These discussions often dwell on intuitions about a single kind of case, but it’s worth seeking a rule that can unify our treatment of (...)
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  24. added 2019-05-16
    What is Conditionalization, and Why Should We Do It?Richard Pettigrew - forthcoming - Philosophical Studies:1-37.
    Conditionalization is one of the central norms of Bayesian epistemology. But there are a number of competing formulations, and a number of arguments that purport to establish it. In this paper, I explore which formulations of the norm are supported by which arguments. In their standard formulations, each of the arguments I consider here depends on the same assumption, which I call Deterministic Updating. I will investigate whether it is possible to amend these arguments so that they no longer depend (...)
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  25. added 2019-05-07
    Imaginary Foundations.Wolfgang Schwarz - 2018 - Ergo: An Open Access Journal of Philosophy 5.
    Our senses provide us with information about the world, but what exactly do they tell us? I argue that in order to optimally respond to sensory stimulations, an agent’s doxastic space may have an extra, “imaginary” dimension of possibility; perceptual experiences confer certainty on propositions in this dimension. To some extent, the resulting picture vindicates the old-fashioned empiricist idea that all empirical knowledge is based on a solid foundation of sense-datum propositions, but it avoids most of the problems traditionally associated (...)
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  26. added 2019-05-07
    Belief Update Across Fission: Figure 1.Wolfgang Schwarz - 2015 - British Journal for the Philosophy of Science 66 (3):659-682.
    When an agent undergoes fission, how should the beliefs of the fission results relate to the pre-fission beliefs? This question is important for the Everett interpretation of quantum mechanics, but it is of independent philosophical interest. Among other things, fission scenarios demonstrate that ‘self-locating’ information can affect the probability of uncentred propositions even if an agent has no essentially self-locating uncertainty. I present a general update rule for centred beliefs that gives sensible verdicts in cases of fission, without relying on (...)
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  27. added 2019-04-01
    Does Roush Show That Evidence Should Be Probable?Damien Fennell & Nancy Cartwright - manuscript
    This paper critically analyzes Sherrilyn Roush’s definition of evidence and especially her powerful defence that in the ideal, a claim should be probable to be evidence for anything. We suggest that Roush treats not one sense of ‘evidence’ but three: relevance, leveraging and grounds for knowledge; and that different parts of her argument fare differently with respect to different senses. For relevance, we argue that probable evidence is sufficient but not necessary for Roush’s own two criteria of evidence to be (...)
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  28. added 2019-02-19
    Bayesian Variations: Essays on the Structure, Object, and Dynamics of Credence.Aron Vallinder - 2018 - Dissertation, London School of Economics
    According to the traditional Bayesian view of credence, its structure is that of precise probability, its objects are descriptive propositions about the empirical world, and its dynamics are given by conditionalization. Each of the three essays that make up this thesis deals with a different variation on this traditional picture. The first variation replaces precise probability with sets of probabilities. The resulting imprecise Bayesianism is sometimes motivated on the grounds that our beliefs should not be more precise than the evidence (...)
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  29. added 2019-02-19
    General Properties of Bayesian Learning as Statistical Inference Determined by Conditional Expectations.Zalán Gyenis & Miklós Rédei - 2017 - Review of Symbolic Logic 10 (4):719-755.
    We investigate the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If (...)
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  30. added 2019-02-19
    Many Irrelevant Evils: A Response to the Bayesian Problem of Evil.Jamie Carlin Watson - 2014 - International Journal of Philosophy and Theology 75 (4):365-378.
    Robert Bass argues that the evidential problem of evil can be strengthened by the application of a Bayesian conditionalization argument. I argue that, whatever the merits of Bayesian conditionalization arguments, they are unsuccessful in substantiating the evidential problem of evil because the problem of evil doesn’t meet the necessary conditions for applying the formula informatively. I offer two examples to show that a successful application of the Bayesian formula must pass two tests, the competency test and the connection test. I (...)
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  31. added 2018-12-18
    Correcting Credences with Chances.Ilho Park - forthcoming - Synthese:1-28.
    Lewis’s Principal Principle is widely recognized as a rationality constraint that our credences should satisfy throughout our epistemic life. In practice, however, our credences often fail to satisfy this principle because of our various epistemic limitations. Facing such violations, we should correct our credences in accordance with this principle. In this paper, I will formulate a way of correcting our credences, which will be called the Adams Correcting Rules and then show that such a rule yields non-commutativity between conditionalizing and (...)
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  32. added 2018-12-18
    Open-Minded Orthodox Bayesianism by Epsilon-Conditionalization.Eric Raidl - 2020 - British Journal for the Philosophy of Science 71 (1):139-176.
    Orthodox Bayesianism endorses revising by conditionalization. This paper investigates the zero-raising problem, or equivalently the certainty-dropping problem of orthodox Bayesianism: previously neglected possibilities remain neglected, although the new evidence might suggest otherwise. Yet, one may want to model open-minded agents, that is, agents capable of raising previously neglected possibilities. Different reasons can be given for open-mindedness, one of which is fallibilism. The paper proposes a family of open-minded propositional revisions depending on a parameter ϵ. The basic idea is this: first (...)
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  33. added 2018-12-18
    A Dutch Book Theorem and Converse Dutch Book Theorem for Kolmogorov Conditionalization.Michael Rescorla - 2018 - Review of Symbolic Logic 11 (4):705-735.
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  34. added 2018-11-15
    Foundations of Probability.Rachael Briggs - 2015 - Journal of Philosophical Logic 44 (6):625-640.
    The foundations of probability are viewed through the lens of the subjectivist interpretation. This article surveys conditional probability, arguments for probabilism, probability dynamics, and the evidential and subjective interpretations of probability.
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  35. added 2018-11-12
    What the "Equal Weight View" Is.Randall G. McCutcheon - manuscript
    Dawid, DeGroot and Mortera showed, a quarter century ago, that any agent who regards a fellow agent as a peer--in particular, defers to the fellow agent's prior credences in the same way that she defers to her own--and updates by split-the-difference is prone to diachronic incoherence. On the other hand one may show that there are special scenarios in which Bayesian updating approximates difference splitting, so it remains an important question whether it remains a viable response to ``generic" peer update. (...)
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  36. added 2018-11-12
    A Non-Pragmatic Dominance Argument for Conditionalization.Robert Williams - manuscript
    In this paper, I provide an accuracy-based argument for conditionalization (via reflection) that does not rely on norms of maximizing expected accuracy. -/- (This is a draft of a paper that I wrote in 2013. It stalled for no very good reason. I still believe the content is right).
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  37. added 2018-10-05
    A Probabilistic Epistemology of Perceptual Belief.Ralph Wedgwood - 2018 - Philosophical Issues 28 (1):1-25.
    There are three well-known models of how to account for perceptual belief within a probabilistic framework: (a) a Cartesian model; (b) a model advocated by Timothy Williamson; and (c) a model advocated by Richard Jeffrey. Each of these models faces a problem—in effect, the problem of accounting for the defeasibility of perceptual justification and perceptual knowledge. It is argued here that the best way of responding to this the best way of responding to this problem effectively vindicates the Cartesian model. (...)
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  38. added 2018-09-10
    Higher-Order Defeat and Doxastic Resilience.Asbjørn Steglich-Petersen - 2019 - In Mattias Skipper & Asbjørn Steglich-Petersen (eds.), Higher-Order Evidence: New Essays. Oxford University Press.
    It seems obvious that when higher-order evidence makes it rational for one to doubt that one’s own belief on some matter is rational, this can undermine the rationality of that belief. This is known as higher-order defeat. However, despite its intuitive plausibility, it has proved puzzling how higher-order defeat works, exactly. To highlight two prominent sources of puzzlement, higher-order defeat seems to defy being understood in terms of conditionalization; and higher-order defeat can sometimes place agents in what seem like epistemic (...)
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  39. added 2018-09-06
    Précis of Accuracy and the Laws of Credence.Richard Pettigrew - 2018 - Philosophy and Phenomenological Research 96 (3):749-754.
  40. added 2018-07-30
    Accuracy, Conditionalization, and Probabilism.Peter J. Lewis & Don Fallis - manuscript
    Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, it seems that conditionalization and probabilism follow from a wide range of measures of inaccuracy. However, we argue that among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, and none from (...)
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  41. added 2018-07-30
    The Modal Logic of Bayesian Belief Revision.Zalán Gyenis, Miklós Rédei & William Brown - 2019 - Journal of Philosophical Logic 48 (5):809-824.
    In Bayesian belief revision a Bayesian agent revises his prior belief by conditionalizing the prior on some evidence using Bayes’ rule. We define a hierarchy of modal logics that capture the logical features of Bayesian belief revision. Elements in the hierarchy are distinguished by the cardinality of the set of elementary propositions on which the agent’s prior is defined. Inclusions among the modal logics in the hierarchy are determined. By linking the modal logics in the hierarchy to the strongest modal (...)
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  42. added 2018-07-30
    On the Modal Logic of Jeffrey Conditionalization.Zalán Gyenis - 2018 - Logica Universalis 12 (3-4):351-374.
    We continue the investigations initiated in the recent papers where Bayes logics have been introduced to study the general laws of Bayesian belief revision. In Bayesian belief revision a Bayesian agent revises his prior belief by conditionalizing the prior on some evidence using the Bayes rule. In this paper we take the more general Jeffrey formula as a conditioning device and study the corresponding modal logics that we call Jeffrey logics, focusing mainly on the countable case. The containment relations among (...)
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  43. added 2018-07-30
    Three Aspects of Typicality in Multiverse Cosmology.Feraz Azhar - unknown
    Extracting predictions from cosmological theories that describe a multiverse, for what we are likely to observe in our domain, is crucial to establishing the validity of these theories. One way to extract such predictions is from theory-generated probability distributions that allow for selection effects---generally expressed in terms of assumptions about anthropic conditionalization and how typical we are. In this paper, I urge three lessons about typicality in multiverse settings. Because it is difficult to characterize our observational situation in the multiverse, (...)
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  44. added 2018-07-30
    The Bayes Blind Spot of a Finite Bayesian Agent is a Large Set.Zalán Gyenis & Miklós Rédei - unknown
    The Bayes Blind Spot of a Bayesian Agent is the set of probability measures on a Boolean algebra that are absolutely continuous with respect to the background probability measure of a Bayesian Agent on the algebra and which the Bayesian Agent cannot learn by conditionalizing no matter what evidence he has about the elements in the Boolean algebra. It is shown that if the Boolean algebra is finite, then the Bayes Blind Spot is a very large set: it has the (...)
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  45. added 2018-07-29
    Monty Hall Drives a Wedge Between Judy Benjamin and the Sleeping Beauty: A Reply to Bovens.Luc Bovens & Jose-Luis Ferreira - 2010 - Analysis 70 (3):473 - 481.
    In “Judy Benjamin is a Sleeping Beauty” (2010) Bovens recognises a certain similarity between the Sleeping Beauty (SB) and the Judy Benjamin (JB). But he does not recognise the dissimilarity between underlying protocols (as spelled out in Shafer (1985). Protocols are expressed in conditional probability tables that spell out the probability of coming to learn various propositions conditional on the actual state of the world. The principle of total evidence requires that we not update on the content of the proposition (...)
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  46. added 2018-07-29
    The Puzzle of the Hats.Luc Bovens & Wlodek Rabinowicz - 2010 - Synthese 172 (1):57-78.
    The Puzzle of the Hats is a betting arrangement which seems to show that a Dutch book can be made against a group of rational players with common priors who act in the common interest and have full trust in the other players’ rationality. But we show that appearances are misleading—no such Dutch book can be made. There are four morals. First, what can be learned from the puzzle is that there is a class of situations in which credences and (...)
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  47. added 2018-07-29
    Judy Benjamin is a Sleeping Beauty.Luc Bovens - 2010 - Analysis 70 (1):23-26.
    I argue that van Fraassen's Judy Benjamin Problem and Elga's Sleeping Beauty Problem have the same structure.
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  48. added 2018-07-22
    Independence Day?Matthew Mandelkern & Daniel Rothschild - 2019 - Journal of Semantics 36 (2):193-210.
    Two recent and influential papers, van Rooij 2007 and Lassiter 2012, propose solutions to the proviso problem that make central use of related notions of independence—qualitative in the first case, probabilistic in the second. We argue here that, if these solutions are to work, they must incorporate an implicit assumption about presupposition accommodation, namely that accommodation does not interfere with existing qualitative or probabilistic independencies. We show, however, that this assumption is implausible, as updating beliefs with conditional information does not (...)
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  49. added 2018-07-12
    Updating for Externalists.J. Dmitri Gallow - forthcoming - Noûs.
    The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In its stead, I (...)
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  50. added 2018-07-09
    Influence of Conditionals on Belief Updating.Borut Trpin - 2018 - Dissertation, University of Ljubljana
    This doctoral dissertation investigates what influence indicative conditionals have on belief updating and how learning from conditionals may be modelled in a probabilistic framework. Because the problem is related to the interpretation of conditionals, we first assess different semantics of indicative conditionals. We propose that conditionals should be taken as primary concepts. This allows us to defend a claim that learning a conditional is equivalent to learning that the relevant conditional probability is 1. This implies that learning a conditional can (...)
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