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  1. Horacio Arlo-Costa, Belief Revision Conditionals: Basic Iterated Systems.
    It is now well known that, on pain of triviality, the probability of a conditional cannot be identified with the corresponding conditional probability [25]. This surprising impossibility result has a qualitative counterpart. In fact, Peter Gärdenfors showed in [13] that believing ‘If A then B’ cannot be equated with the act of believing B on the supposition that A — as long as supposing obeys minimal Bayesian constraints. Recent work has shown that in spite of these negative results, the question (...)
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  2. Brad Armendt (1992). Dutch Strategies for Diachronic Rules: When Believers See the Sure Loss Coming. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:217 - 229.
    Two criticisms of Dutch strategy arguments are discussed: One says that the arguments fail because agents who know the arguments can use that knowledge to avoid Dutch strategy vulnerability, even though they violate the norm in question. The second consists of cases alleged to be counterexamples to the norms that Dutch strategy arguments defend. The principle of Reflection and its Dutch strategy argument are discussed, but most attention is given to the rule of Conditionalization and to Jeffrey's rule for fallible (...)
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  3. Frank Arntzenius (2003). Self-Locating Beliefs, Reflection, Conditionalization and Dutch Books. Journal of Philosophy 100:356-370.
  4. David Atkinson & Jeanne Peijnenburg (2008). Reichenbach's Posits Reposited. Erkenntnis 69 (1):93 - 108.
    Reichenbach’s use of ‘posits’ to defend his frequentistic theory of probability has been criticized on the grounds that it makes unfalsifiable predictions. The justice of this criticism has blinded many to Reichenbach’s second use of a posit, one that can fruitfully be applied to current debates within epistemology. We show first that Reichenbach’s alternative type of posit creates a difficulty for epistemic foundationalists, and then that its use is equivalent to a particular kind of Jeffrey conditionalization. We conclude that, under (...)
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  5. Fahiem Bacchus, Henry E. Kyburg Jr & Mariam Thalos (1990). Against Conditionalization. Synthese 85 (3):475 - 506.
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  6. Alexandru Baltag & Sonja Smets (2008). Probabilistic Dynamic Belief Revision. Synthese 165 (2):179 - 202.
    We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plausibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of “knowledge”. We develop a probabilistic version of this concept (“degree of safety”) and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief (...)
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  7. Paul Bartha, How to Put Self-Locating Information in its Place.
    How can self-locating propositions be integrated into normal patterns of belief revision? Puzzles such as Sleeping Beauty seem to show that such propositions lead to violation of ordinary principles for reasoning with subjective probability, such as Conditionalization and Reflection. I show that sophisticated forms of Conditionalization and Reflection are not only compatible with self-locating propositions, but also indispensable in understanding how they can function as evidence in Sleeping Beauty and similar cases.
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  8. Johan van Benthem, Jelle Gerbrandy & Barteld Kooi (2009). Dynamic Update with Probabilities. Studia Logica 93 (1):67 - 96.
    Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic (...)
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  9. Nick Bostrom (2007). Sleeping Beauty and Self-Location: A Hybrid Model. Synthese 157 (1):59 - 78.
    The Sleeping Beauty problem is test stone for theories about self- locating belief, i.e. theories about how we should reason when data or theories contain indexical information. Opinion on this problem is split between two camps, those who defend the “1/2 view” and those who advocate the “1/3 view”. I argue that both these positions are mistaken. Instead, I propose a new “hybrid” model, which avoids the faults of the standard views while retaining their attractive properties. This model appears to (...)
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  10. Craig Boutilier (1995). On the Revision of Probabilistic Belief States. Notre Dame Journal of Formal Logic 36 (1):158-183.
    In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function determines the nature of the revised function. The first is based on a probabilistic extension of Spohn's OCFs, whereas the second exploits the structure implicit in the Popper function itself. This stands in contrast with (...)
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  11. Richard Bradley (2005). Radical Probabilism and Bayesian Conditioning. Philosophy of Science 72 (2):342-364.
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one’s (...)
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  12. Aaron Bronfman (forthcoming). Conditionalization and Not Knowing That One Knows. Erkenntnis:1-22.
    Bayesian Conditionalization is a widely used proposal for how to update one’s beliefs upon the receipt of new evidence. This is in part because of its attention to the totality of one’s evidence, which often includes facts about what one’s new evidence is and how one has come to have it. However, an increasingly popular position in epistemology holds that one may gain new evidence, construed as knowledge, without being in a position to know that one has gained this evidence. (...)
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  13. Vincent Conitzer (forthcoming). A Devastating Example for the Halfer Rule. Philosophical Studies:1-8.
    How should we update de dicto beliefs in the face of de se evidence? The Sleeping Beauty problem divides philosophers into two camps, halfers and thirders. But there is some disagreement among halfers about how their position should generalize to other examples. A full generalization is not always given; one notable exception is the Halfer Rule, under which the agent updates her uncentered beliefs based on only the uncentered part of her evidence. In this brief article, I provide a simple (...)
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  14. Eleonora Cresto (2010). Belief and Contextual Acceptance. Synthese 177 (1):41-66.
    I develop a strategy for representing epistemic states and epistemic changes that seeks to be sensitive to the difference between voluntary and involuntary aspects of our epistemic life, as well as to the role of pragmatic factors in epistemology. The model relies on a particular understanding of the distinction between full belief and acceptance , which makes room for the idea that our reasoning on both practical and theoretical matters typically proceeds in a contextual way. Within this framework, I discuss (...)
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  15. Charles B. Cross (2000). A Characterization of Imaging in Terms of Popper Functions. Philosophy of Science 67 (2):316-338.
    Despite the results of David Lewis, Peter Gärdenfors, and others, showing that imaging and classical conditionalization coincide only in the most trivial probabilistic models of belief revision, it turns out that imaging on a proposition A can always be described via Popper function conditionalization on a proposition that entails A. This result generalizes to any method of belief revision meeting certain minimal requirements. The proof is illustrated by an application of imaging in the context of the Monty Hall Problem.
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  16. Persi Diaconis & Sandy L. Zabell (1982). Updating Subjective Probability. Journal of the American Statistical Association 77 (380):822-830.
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  17. Dennis Dieks (2007). Reasoning About the Future: Doom and Beauty. [REVIEW] Synthese 156 (3):427 - 439.
    According to the Doomsday Argument we have to rethink the probabilities we assign to a soon or not so soon extinction of mankind when we realize that we are living now, rather early in the history of mankind. Sleeping Beauty finds herself in a similar predicament: on learning the date of her first awakening, she is asked to re-evaluate the probabilities of her two possible future scenarios. In connection with Doom, I argue that it is wrong to assume that our (...)
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  18. Zoltan Domotor (1980). Probability Kinematics and Representation of Belief Change. Philosophy of Science 47 (3):384-403.
    Bayesian, Jeffrey and Field conditionals are compared and it is shown why the last two cannot be reduced to the first. Maximum relative entropy is used in two kinds of justification of the Field conditional and the dispensability of entropy principles in general is discussed.
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  19. Frank Döring (2000). Conditional Probability and Dutch Books. Philosophy of Science 67 (3):391-409.
    There is no set Δ of probability axioms that meets the following three desiderata: (1) Δ is vindicated by a Dutch book theorem; (2) Δ does not imply regularity (and thus allows, among other things, updating by conditionalization); (3) Δ constrains the conditional probability q(·,z) even when the unconditional probability p(z) (=q(z,T)) equals 0. This has significant consequences for Bayesian epistemology, some of which are discussed.
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  20. Frank Döring (1999). Why Bayesian Psychology is Incomplete. Philosophy of Science 66 (3):389.
    Bayesian psychology, in what is perhaps its most familiar version, is incomplete: Jeffrey conditionalization is not a complete account of rational belief change. Jeffrey conditionalization is sensitive to the order in which the evidence arrives. This order effect can be so pronounced as to call for a belief adjustment that cannot be understood as an assimilation of incoming evidence by Jeffrey's rule. Hartry Field's reparameterization of Jeffrey's rule avoids the order effect but fails as an account of how new evidence (...)
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  21. Frank Doring (1999). Why Bayesian Psychology Is Incomplete. Philosophy of Science 66 (S1):S379 - S389.
    Bayesian psychology, in what is perhaps its most familiar version, is incomplete: Jeffrey conditionalization is not a complete account of rational belief change. Jeffrey conditionalization is sensitive to the order in which the evidence arrives. This order effect can be so pronounced as to call for a belief adjustment that cannot be understood as an assimilation of incoming evidence by Jeffrey's rule. Hartry Field's reparameterization of Jeffrey's rule avoids the order effect but fails as an account of how new evidence (...)
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  22. Jeffrey Dunn (2010). Bayesian Epistemology and Having Evidence. 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 must have (...)
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  23. John Earman (ed.) (1984). Testing Scientific Theories. Minneapolis: University of Minnesota Press.
    Rich with historical and cultural value, these works are published unaltered from the original University of Minnesota Press editions.
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  24. Damien Fennell & Nancy Cartwright (2010). Does Roush Show That Evidence Should Be Probable? Synthese 175 (3):289 - 310.
    This paper critically analyzes Sherrilyn Roush's (Tracking truth: knowledge, evidence and science, 2005) 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 (...)
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  25. Eduardo Fermé & Ricardo Rodríguez (2006). DFT and Belief Revision. Análisis Filosófico 26 (2):373-393.
    Alchourrón devoted his last years to the analysis of the notion of defeasible conditionalization. He developed a formal system capturing the essentials of this notion. His definition of the defeasible conditional is given in terms of strict implication operator and a modal operator f which is interpreted as a revision function at the language level. In this paper, we will point out that this underlying revision function is more general than the well known AGM revision [4]. In addition, we will (...)
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  26. Bas C. Van Fraassen (2006). Vague Expectation Value Loss. Philosophical Studies 127 (3):483 - 491.
    Vague subjective probability may be modeled by means of a set of probability functions, so that the represented opinion has only a lower and upper bound. The standard rule of conditionalization can be straight-forwardly adapted to this. But this combination has difficulties which, though well known in the technical literature, have not been given sufficient attention in probabilist or Bayesian epistemology. Specifically, updating on apparently irrelevant bits of news can be destructive of one's explicitly prior expectations. Stability of (...) subjective opinion appears to need a more complex model. (shrink)
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  27. Bas C. Van Fraassen (2005). Conditionalizing on Violated Bell's Inequalities. Analysis 65 (1):27 - 32.
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  28. Bas C. Van Fraassen (1980). Rational Belief and Probability Kinematics. Philosophy of Science 47 (2):165 - 187.
    A general form is proposed for epistemological theories, the relevant factors being: the family of epistemic judgments, the epistemic state, the epistemic commitment (governing change of state), and the family of possible epistemic inputs (deliverances of experience). First a simple theory is examined in which the states are probability functions, and the subject of probability kinematics introduced by Richard Jeffrey is explored. Then a second theory is examined in which the state has as constituents a body of information (...)
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  29. Haim Gaifman (2012). Deceptive Updating and Minimal Information Methods. Synthese 187 (1):147 - 178.
    The technique of minimizing information (infomin) has been commonly employed as a general method for both choosing and updating a subjective probability function. We argue that, in a wide class of cases, the use of infomin methods fails to cohere with our standard conception of rational degrees of belief. We introduce the notion of a deceptive updating method and argue that non-deceptiveness is a necessary condition for rational coherence. Infomin has been criticized on the grounds that there are no higher (...)
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  30. J. Dmitri Gallow (2014). How to Learn From Theory-Dependent Evidence; or Commutativity and Holism: A Solution for Conditionalizers. British Journal for the Philosophy of Science 65 (3):493-519.
    Weisberg ([2009]) provides an argument that neither conditionalization nor Jeffrey conditionalization is capable of accommodating the holist’s claim that beliefs acquired directly from experience can suffer undercutting defeat. I diagnose this failure as stemming from the fact that neither conditionalization nor Jeffrey conditionalization give any advice about how to rationally respond to theory-dependent evidence, and I propose a novel updating procedure that does tell us how to respond to evidence like this. This holistic updating rule yields conditionalization as a special (...)
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  31. Paul R. Graves (1989). The Total Evidence Theorem for Probability Kinematics. Philosophy of Science 56 (2):317-324.
    L. J. Savage and I. J. Good have each demonstrated that the expected utility of free information is never negative for a decision maker who updates her degrees of belief by conditionalization on propositions learned for certain. In this paper Good's argument is generalized to show the same result for a decision maker who updates her degrees of belief on the basis of uncertain information by Richard Jeffrey's probability kinematics. The Savage/Good result is shown to be a special case of (...)
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  32. Berry Groisman (2008). The End of Sleeping Beauty's Nightmare. British Journal for the Philosophy of Science 59 (3):409-416.
    The way a rational agent changes her belief in certain propositions/hypotheses in the light of new evidence lies at the heart of Bayesian inference. The basic natural assumption, as summarized in van Fraassen's Reflection Principle ([1984]), would be that in the absence of new evidence the belief should not change. Yet, there are examples that are claimed to violate this assumption. The apparent paradox presented by such examples, if not settled, would demonstrate the inconsistency and/or incompleteness of the Bayesian approach, (...)
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  33. Jr: Henry E. Kyburg (1990). Probabilistic Inference and Probabilistic Reasoning. Philosophical Topics 18 (2):107-116.
  34. Terence Horgan (forthcoming). Generalized Conditionalization and the Sleeping Beauty Problem, II. Erkenntnis.
    In “Generalized Conditionalization and the Sleeping Beauty Problem,” Anna Mahtani and I offer a new argument for thirdism that relies on what we call “generalized conditionalization.” Generalized conditionalization goes beyond conventional conditionalization in two respects: first, by sometimes deploying a space of synchronic, essentially temporal, candidate-possibilities that are not “prior” possibilities; and second, by allowing for the use of preliminary probabilities that arise by first bracketing, and then conditionalizing upon, “old evidence.” In “Beauty and Conditionalization: Reply to Horgan and Mahtani,” (...)
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  35. Terry Horgan & Anna Mahtani (2013). Generalized Conditionalization and the Sleeping Beauty Problem. Erkenntnis 78 (2):333-351.
    We present a new argument for the claim that in the Sleeping Beauty problem, the probability that the coin comes up heads is 1/3. Our argument depends on a principle for the updating of probabilities that we call ‘generalized conditionalization’, and on a species of generalized conditionalization we call ‘synchronic conditionalization on old information’. We set forth a rationale for the legitimacy of generalized conditionalization, and we explain why our new argument for thirdism is immune to two attacks that Pust (...)
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  36. Colin Howson (1992). Dutch Book Arguments and Consistency. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:161 - 168.
    I consider Dutch Book arguments for three principles of classical Bayesianism: (i) agents' belief-probabilities are consistent only if they obey the probability axioms. (ii) beliefs are updated by Bayesian conditionalisation. (iii) that the so-called Principal Principle connects statistical and belief probabilities. I argue that while there is a sound Dutch Book argument for (i), the standard ones for (ii) based on the Lewis-Teller strategy are unsound, for reasons pointed out by Christensen. I consider a type of Dutch Book argument for (...)
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  37. Colin Howson & Peter Urbach (1993). Scientific Reasoning: The Bayesian Approach. Open Court.
    Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...
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  38. Franz Huber, Formal Representations of Belief. Stanford Encyclopedia of Philosophy.
    Epistemology is the study of knowledge and justified belief. Belief is thus central to epistemology. It comes in a qualitative form, as when Sophia believes that Vienna is the capital of Austria, and a quantitative form, as when Sophia's degree of belief that Vienna is the capital of Austria is at least twice her degree of belief that tomorrow it will be sunny in Vienna. Formal epistemology, as opposed to mainstream epistemology (Hendricks 2006), is epistemology done in a formal way, (...)
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  39. Franz Huber (2008). Reply to Crupi Et Al.'S "Bayesian Confirmation by Uncertain Evidence&Quot;. British Journal for the Philosophy of Science 59 (2):213 - 215.
    Crupi et al. ([2008]) propose a generalization of Bayesian confirmation theory that they claim to adequately deal with confirmation by uncertain evidence. Consider a series of points of time t0, . . . , ti, . . . , tn such that the agent’s subjective probability for an atomic proposition E changes from Pr0(E) at t0 to . . . to Pri(E) at ti to . . . to Prn(E) at tn. It is understood that the agent’s subjective probabilities change (...)
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  40. R. I. G. Hughes & Bas C. Van Fraassen (1984). Symmetry Arguments in Probability Kinematics. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:851 - 869.
    Probability kinematics is the theory of how subjective probabilities change with time, in response to certain constraints (accepted by the subject). Rules are classified by the imposed constraints for which the rules prescribe a procedure for updating one's opinion. The first is simple conditionalization (constraint: give probability 1 to proposition A), and the second Jeffrey conditionalization (constraint: give probability x i , 0 i ). It is demonstrated by a symmetry argument that these rules are the unique admissible rules for (...)
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  41. Simon M. Huttegger (2014). Learning Experiences and the Value of Knowledge. Philosophical Studies 171 (2):279-288.
    Generalized probabilistic learning takes place in a black-box where present probabilities lead to future probabilities by way of a hidden learning process. The idea that generalized learning can be partially characterized by saying that it doesn’t foreseeably lead to harmful decisions is explored. It is shown that a martingale principle follows for finite probability spaces.
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  42. Richard Jeffrey (1983). The Logic of Decision. University of Chicago Press.
    "Frederic Schick, Journal of Philosophy This book uses elementary logical and mathematical means to philosophical end: elucidation of the notions of subjective ...
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  43. Richard C. Jeffrey (1983). Bayesianism With A Human Face. In John Earman (ed.), Testing Scientific Theories. University of Minnesota Press. 133--156.
  44. Richard C. Jeffrey (1975). Carnap's Empiricism. Minnesota Studies in the Philosophy of Science 6.
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  45. Karl Karlander & Levi Spectre (2010). Sleeping Beauty Meets Monday. Synthese 174 (3):397 - 412.
    The Sleeping Beauty problem—first presented by A. Elga in a philosophical context—has captured much attention. The problem, we contend, is more aptly regarded as a paradox: apparently, there are cases where one ought to change one’s credence in an event’s taking place even though one gains no new information or evidence, or alternatively, one ought to have a credence other than 1/2 in the outcome of a future coin toss even though one knows that the coin is fair. In this (...)
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  46. Namjoong Kim, Sleeping Beauty and De Nunc Updating.
    About a decade ago, Adam Elga introduced philosophers to an intriguing puzzle. In it, Sleeping Beauty, a perfectly rational agent, undergoes an experiment in which she becomes ignorant of what time it is. This situation is puzzling for two reasons: First, because there are two equally plausible views about how she will change her degree of belief given her situation and, second, because the traditional rules for updating degrees of belief don't seem to apply to this case. In this dissertation, (...)
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  47. Matthew Kopec (2012). We Ought to Agree: A Consequence of Repairing Goldman's Group Scoring Rule. Episteme 9 (2):101-114.
    In Knowledge in a Social World, Alvin Goldman presents a framework to quantify the epistemic effects that various policies, procedures, and behaviors can have on a group of agents. In this essay, I show that the framework requires some modifications when applied to agents with credences. The required modifications carry with them an interesting consequence, namely, that any group whose members disagree can become more accurate by forming a consensus through averaging their credences. I sketch a way that this result (...)
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  48. Theo A. F. Kuipers (1984). Inductive Analogy in Carnapian Spirit. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:157 - 167.
    In this paper it is shown that there is a natural way of dealing with analogy by similarity in inductive systems by extending intuitive ways of introduction of systems without analogy. This procedure leads to Carnap-like systems, with zero probability for contingent generalizations, satisfying a general principle of so-called virtual analogy. This new principle is different from, but compatible with, Carnap's principle. It will be shown that the latter principle is satisfied, and should only be satisfied, if the underlying distance (...)
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  49. Peter Kung (2010). On Having No Reason: Dogmatism and Bayesian Confirmation. Synthese 177 (1):1 - 17.
    Recently in epistemology a number of authors have mounted Bayesian objections to dogmatism. These objections depend on a Bayesian principle of evidential confirmation: Evidence E confirms hypothesis H just in case Pr(H|E) > Pr(H). I argue using Keynes' and Knight's distinction between risk and uncertainty that the Bayesian principle fails to accommodate the intuitive notion of having no reason to believe. Consider as an example an unfamiliar card game: at first, since you're unfamiliar with the game, you assign credences based (...)
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  50. Jürgen Landes & Jon Williamson, Objective Bayesianism and the Maximum Entropy Principle.
    Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities, they should be calibrated to our evidence of physical probabilities, and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism (...)
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