About this topic

In Bayesian epistemology, an updating principle is a principle that specifies or puts restrictions on the changes in an agent’s belief state that follow (or should follow) some initial change in the agent’s belief state (usually – but maybe not always – as a result of the agent being exposed to new evidence). Although in other academic fields a great deal of the discussion regarding updating principles touches upon their empirical fit to the way people actually update their beliefs, much of the relevant philosophical literature is normative. The central questions are whether, why and in which contexts, obeying different updating principles is rationally required. In the simplest (but not uncommon) case, where the agent’s belief state can be represented by a single probability distribution over a set of propositions, and the initial change is that of learning a new proposition (represented as raising the probability of the learnt proposition to 1), the most popular updating rule is Bayesian Conditionalization. Richard Jeffrey offered a generalization of Bayesian Conditionalization, usually called “Jeffrey’s conditionalization”, to cases in which, although there is some initial change in the agent’s belief state, the probability of no proposition in the set is raised to 1. Others have introduced, discussed and explored the formal features of other updating principles. These principles are usually ones that either cover cases to which Jeffrey’s conditionalization does not apply (such as cases of “growing awareness” in which the initial change is represented as an addition of new propositions to the set or cases in which the agent’s initial belief set cannot be represented by a single probability distribution over a set of propositions) or constitute generalizations of or alternatives to Bayesian Conditionalization and Jeffrey’s Conditionalization in specific contexts (such as Adams’ conditionalization for the case of learning conditional probabilities, Imaging which – in some contexts – seem to fit better with other intuitive epistemic principles or different types of pooling methods for the case of learning other agents’ beliefs).

Key works

Jeffrey 1992 introduces the idea of probability kinematics and discusses its features. Some important discussions of Jeffrey’s rule include Field 1978, Fraassen 1980 and Skyrms 1987. Bradley 2005 introduces and discusses Adams’ Conditionalization. In Bradley 2017 Bradley also discusses growing awareness and the relation between belief updating and the updating of desires and preferences. “Imaging” was introduced and discussed in Lewis 1976 and Gardenfors 1982. Leitgeb 2017 discusses the relation between Imaging and different belief aggregation methods. Some discussions of different problems associated with updating imprecise probabilities are White 2009, Bradley & Steele 2014 and Joyce 2010.

Introductions Jeffrey 2004
Related categories

324 found
1 — 50 / 324
  1. added 2018-12-18
    Open-Minded Orthodox Bayesianism by Epsilon-Conditionalization.Eric Raidl - forthcoming - British Journal for the Philosophy of Science.
  2. added 2018-11-12
    Holistic Conditionalization and Underminable Perceptual Learning.Brian T. Miller - forthcoming - Philosophy and Phenomenological Research.
    Seeing a red hat can (i) increase my credence in the hat is red, and (ii) introduce a negative dependence between that proposition and po- tential undermining defeaters such as the light is red. The rigidity of Jeffrey Conditionalization makes this awkward, as rigidity preserves inde- pendence. The picture is less awkward given ‘Holistic Conditionalization’, or so it is claimed. I defend Jeffrey Conditionalization’s consistency with underminable perceptual learning and its superiority to Holistic Conditionalization, arguing that the latter is merely (...)
  3. added 2018-11-05
    Self‐Locating Evidence and the Metaphysics of Time.David Builes - forthcoming - Philosophy and Phenomenological Research.
    I argue that different views in the metaphysics of time make different observational predictions in both classical and relativistic cases. Because different views in the metaphysics of time differ over which facts are merely indexical facts, they make different observational predictions about certain self-locating propositions. I argue for this thesis by distinguishing the three main updating procedures that apply in cases of self-locating uncertainty, and I present a series of cases which cumulatively show that every one of these updating procedures (...)
  4. added 2018-11-05
    Anchoring in Deliberations.Stephan Hartmann & Soroush Rafiee Rad - forthcoming - Erkenntnis:1-29.
    Deliberation is a standard procedure for making decisions in not too large groups. It has the advantage that group members can learn from each other and that, at the end, often a consensus emerges that everybody endorses. Unfortunately, however, implementing a deliberation procedure also has a number of disadvantages due to the cognitive limitations of the individual group members. What is more, the very process of deliberation introduces an additional bias, which we investigate in this article. We demonstrate that even (...)
  5. added 2018-11-05
    Higher-Order Beliefs and the Undermining Problem for Bayesianism.Lisa Cassell - forthcoming - Acta Analytica:1-17.
    Jonathan Weisberg has argued that Bayesianism’s rigid updating rules make Bayesian updating incompatible with undermining defeat. In this paper, I argue that when we attend to the higher-order beliefs we must ascribe to agents in the kinds of cases Weisberg considers, the problem he raises disappears. Once we acknowledge the importance of higher-order beliefs to the undermining story, we are led to a different understanding of how these cases arise. And on this different understanding of things, the rigid nature of (...)
  6. added 2018-11-05
    The Art of Learning.Jason Konek - unknown
    Confirmational holism is at odds with Jeffrey conditioning --- the orthodox Bayesian policy for accommodating uncertain learning experiences. Two of the great insights of holist epistemology are that the effects of experience ought to be mediated by one's background beliefs, and the support provided by one's learning experience can and often is undercut by subsequent learning. Jeffrey conditioning fails to vindicate either of these insights. My aim is to describe and defend a new updating policy that does better. In addition (...)
  7. added 2018-11-05
    Is There a Place in Bayesian Confirmation Theory for the Reverse Matthew Effect?William Roche - 2018 - Synthese 195 (4):1631-1648.
    Bayesian confirmation theory is rife with confirmation measures. Many of them differ from each other in important respects. It turns out, though, that all the standard confirmation measures in the literature run counter to the so-called “Reverse Matthew Effect” (“RME” for short). Suppose, to illustrate, that H1 and H2 are equally successful in predicting E in that p(E | H1)/p(E) = p(E | H2)/p(E) > 1. Suppose, further, that initially H1 is less probable than H2 in that p(H1) < p(H2). (...)
  8. added 2018-11-05
    Cognitive Mobile Homes.Daniel Greco - 2017 - Mind 126 (501):93-121.
    While recent discussions of contextualism have mostly focused on other issues, some influential early statements of the view emphasized the possibility of its providing an alternative to both coherentism and traditional versions of foundationalism. In this essay, I will pick up on this strand of contextualist thought, and argue that contextualist versions of foundationalism promise to solve some problems that their non-contextualist cousins cannot. In particular, I will argue that adopting contextualist versions of foundationalism can let us reconcile Bayesian accounts (...)
  9. added 2018-11-05
    Imaging Uncertainty.Benjamin Eva & Stephan Hartmann - unknown
    The technique of imaging was first introduced by Lewis, in order to provide a novel account of the probability of conditional propositions. In the intervening years, imaging has been the object of significant interest in both AI and philosophy, and has come to be seen as a philosophically important approach to probabilistic updating and belief revision. In this paper, we consider the possibility of generalising imaging to deal with uncertain evidence and partial belief revision. In particular, we introduce a new (...)
  10. added 2018-11-05
    Bayesianism as a Set of Meta-Criteria and Its Social Application.Tetsuji Iseda - unknown
    This paper aims at giving a general outlook of Bayesianism as a set of meta-criteria for scientific methodology. In particular, it discusses Social Bayesianism, that is, the application of Bayesian meta-criteria to scientific institutions. From a Bayesian point of view, methodologies and institutions that simulate Bayesian belief updating are good ones, and those with more discriminatory power are better ones than those with less discriminatory power, other things being equal. This paper applies these ideas to a particular issue: diversity in (...)
  11. added 2018-11-05
    Preference Change.Anaïs Cadilhac, Nicholas Asher, Alex Lascarides & Farah Benamara - 2015 - Journal of Logic, Language and Information 24 (3):267-288.
    Most models of rational action assume that all possible states and actions are pre-defined and that preferences change only when beliefs do. But several decision and game problems lack these features, calling for a dynamic model of preferences: preferences can change when unforeseen possibilities come to light or when there is no specifiable or measurable change in belief. We propose a formally precise dynamic model of preferences that extends an existing static model. Our axioms for updating preferences preserve consistency while (...)
  12. added 2018-11-05
    Agreeing to Disagree in Probabilistic Dynamic Epistemic Logic.Lorenz Demey - 2014 - Synthese 191 (3):409-438.
    This paper studies Aumann’s agreeing to disagree theorem from the perspective of dynamic epistemic logic. This was first done by Dégremont and Roy (J Phil Log 41:735–764, 2012) in the qualitative framework of plausibility models. The current paper uses a probabilistic framework, and thus stays closer to Aumann’s original formulation. The paper first introduces enriched probabilistic Kripke frames and models, and various ways of updating them. This framework is then used to prove several agreement theorems, which are natural formalizations of (...)
  13. added 2018-11-05
    Categorical Induction From Uncertain Premises: Jeffrey's Doesn't Completely Rule.Constantinos Hadjichristidis, Steven A. Sloman & David E. Over - 2014 - Thinking and Reasoning 20 (4):405-431.
    Studies of categorical induction typically examine how belief in a premise (e.g., Falcons have an ulnar artery) projects on to a conclusion (e.g., Robins have an ulnar artery). We study induction in cases in which the premise is uncertain (e.g., There is an 80% chance that falcons have an ulnar artery). Jeffrey's rule is a normative model for updating beliefs in the face of uncertain evidence. In three studies we tested the descriptive validity of Jeffrey's rule and a related probability (...)
  14. added 2018-11-05
    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 justify a given (...)
  15. added 2018-11-05
    Learning Conditional Information.Igor Douven - 2012 - Mind and Language 27 (3):239-263.
    Some of the information we receive comes to us in an explicitly conditional form. It is an open question how to model the accommodation of such information in a Bayesian framework. This paper presents data suggesting that there may be no strictly Bayesian account of updating on conditionals. Specifically, the data seem to indicate that such updating at least sometimes proceeds on the basis of explanatory considerations, which famously have no home in standard Bayesian epistemology. The paper also proposes a (...)
  16. added 2018-11-05
    Social Deliberation: Nash, Bayes, and the Partial Vindication of Gabriele Tarde.J. McKenzie Alexander - 2009 - Episteme 6 (2):164-184.
    At the very end of the 19th century, Gabriele Tarde wrote that all society was a product of imitation and innovation. This view regarding the development of society has, to a large extent, fallen out of favour, and especially so in those areas where the rational actor model looms large. I argue that this is unfortunate, as models of imitative learning, in some cases, agree better with what people actually do than more sophisticated models of learning. In this paper, I (...)
  17. added 2018-11-05
    A Unified Bayesian Decision Theory.Richard Bradley - 2007 - Theory and Decision 63 (3):233-263,.
    This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be derived as (...)
  18. added 2018-11-05
    Generalization, Similarity, and Bayesian Inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
  19. added 2018-11-05
    What Are Conditional Probabilities Conditional Upon?K. Hutchison - 1999 - British Journal for the Philosophy of Science 50 (4):665-695.
    This paper rejects a traditional epistemic interpretation of conditional probability. Suppose some chance process produces outcomes X, Y,..., with probabilities P(X), P(Y),... If later observation reveals that outcome Y has in fact been achieved, then the probability of outcome X cannot normally be revised to P(X|Y) ['P&Y)/P(Y)]. This can only be done in exceptional circumstances - when more than just knowledge of Y-ness has been attained. The primary reason for this is that the weight of a piece of evidence varies (...)
  20. added 2018-11-05
    Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84:57-112.
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...)
  21. added 2018-11-05
    Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory.John Earman - 1992 - MIT Press.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In (...)
  22. added 2018-11-05
    Getting Fancy with Probability.Henry E. Kyburg Jr - 1992 - Synthese 90 (2):189 - 203.
    There are a number of reasons for being interested in uncertainty, and there are also a number of uncertainty formalisms. These formalisms are not unrelated. It is argued that they can all be reflected as special cases of the approach of taking probabilities to be determined by sets of probability functions defined on an algebra of statements. Thus, interval probabilities should be construed as maximum and minimum probabilities within a set of distributions, Glenn Shafer's belief functions should be construed as (...)
  23. added 2018-10-01
    An Accuracy‐Dominance Argument for Conditionalization.R. A. Briggs & Richard Pettigrew - forthcoming - Noûs.
  24. added 2018-10-01
    A Puzzle About Experts, Evidential Screening-Off and Conditionalization.Ittay Nissan-Rozen - forthcoming - Episteme:1-9.
    I present a puzzle about the epistemic role beliefs about experts' beliefs play in a rational agent's system of beliefs. It is shown that accepting the claim that an expert's degree of belief in a proposition, A, screens off the evidential support another proposition, B, gives to A in case the expert knows and is certain about whether B is true, leads in some cases to highly unintuitive conclusions. I suggest a solution to the puzzle according to which evidential screening (...)
  25. added 2018-10-01
    A Pragmatic Argument Against Equal Weighting.Ittay Nissan-Rozen & Levi Spectre - forthcoming - Synthese.
    We present a minimal pragmatic restriction on the interpretation of the weights in the “Equal Weight View” (and, more generally, in the “Linear Pooling” view) regarding peer disagreement and show that the view cannot respect it. Based on this result we argue against the view. The restriction is the following one: if an agent, i, assigns an equal or higher weight to another agent, j, (i.e. if i takes j to be as epistemically competent as him or epistemically superior to (...)
  26. added 2018-10-01
    Reasoning with Comparative Moral Judgements: An Argument for Moral Bayesianism.Ittay Nissan-Rozen - 2017 - In Rafal Urbaniak & Gillman Payette (eds.), Applications of Formal Philosophy - The Road Less Travelled. Cham: Springer. pp. 113-136.
    The paper discusses the notion of reasoning with comparative moral judgements (i.e judgements of the form “act a is morally superior to act b”) from the point of view of several meta-ethical positions. Using a simple formal result, it is argued that only a version of moral cognitivism that is committed to the claim that moral beliefs come in degrees can give a normatively plausible account of such reasoning. Some implications of accepting such a version of moral cognitivism are discussed.
  27. added 2018-10-01
    Conditionalization Does Not Maximize Expected Accuracy.Miriam Schoenfield - 2017 - Mind 126 (504):1155-1187.
    Greaves and Wallace argue that conditionalization maximizes expected accuracy. In this paper I show that their result only applies to a restricted range of cases. I then show that the update procedure that maximizes expected accuracy in general is one in which, upon learning P, we conditionalize, not on P, but on the proposition that we learned P. After proving this result, I provide further generalizations and show that much of the accuracy-first epistemology program is committed to KK-like iteration principles (...)
  28. added 2018-10-01
    Presupposition Projection and Conditionalization.Amaia Garcia-Odon - 2016 - Topoi 35 (1):145-156.
    I explain what exactly constrains presupposition projection in compound sentences and argue that the presuppositions that do not project are conditionalized, giving rise to inferable conditional presuppositions. I combine elements of and which, together with an additional, independently motivated assumption, make it possible to construct an analysis that makes correct predictions. The core of my proposal is as follows: When a speaker felicitously utters a compound sentence whose constituent clauses require presuppositions, the hearer will infer that the speaker presupposes those (...)
  29. added 2018-10-01
    Reflection, Conditionalization and Indeterminacy About the Future.Michael J. Shaffer - 2014 - The Reasoner 8:65-66.
    This paper shows that any view of future contingent claims that treats such claims as having indeterminate truth values or as simply being false implies probabilistic irrationality. This is because such views of the future imply violations of reflection, special reflection and conditionalization.
  30. added 2018-10-01
    Conditionalization and Essentially Indexical Credence.Joel Pust - 2012 - Journal of Philosophy 109 (4):295-315.
    One can have no prior credence whatsoever (not even zero) in a temporally indexical claim. This fact saves the principle of conditionalization from potential counterexample and undermines the Elga and Arntzenius/Dorr arguments for the thirder position and Lewis' argument for the halfer position on the Sleeping Beauty Problem, thereby supporting the double-halfer position. -/- .
  31. added 2018-10-01
    Self-Location is No Problem for Conditionalization.Darren Bradley - 2011 - Synthese 182 (3):393-411.
    How do temporal and eternal beliefs interact? I argue that acquiring a temporal belief should have no effect on eternal beliefs for an important range of cases. Thus, I oppose the popular view that new norms of belief change must be introduced for cases where the only change is the passing of time. I defend this position from the purported counter-examples of the Prisoner and Sleeping Beauty. I distinguish two importantly different ways in which temporal beliefs can be acquired and (...)
  32. added 2018-10-01
    Bayesianism II: Applications and Criticisms.Kenny Easwaran - 2011 - Philosophy Compass 6 (5):321-332.
    In the first paper, I discussed the basic claims of Bayesianism (that degrees of belief are important, that they obey the axioms of probability theory, and that they are rationally updated by either standard or Jeffrey conditionalization) and the arguments that are often used to support them. In this paper, I will discuss some applications these ideas have had in confirmation theory, epistemol- ogy, and statistics, and criticisms of these applications.
  33. added 2018-10-01
    Triviality Pursuit.Alan Hájek - 2011 - Topoi 30 (1):3-15.
    The thesis that probabilities of conditionals are conditional probabilities has putatively been refuted many times by so-called ‘triviality results’, although it has also enjoyed a number of resurrections. In this paper I assault it yet again with a new such result. I begin by motivating the thesis and discussing some of the philosophical ramifications of its fluctuating fortunes. I will canvas various reasons, old and new, why the thesis seems plausible, and why we should care about its fate. I will (...)
  34. added 2018-10-01
    Sleeping Beauty Meets Monday.Karl Karlander & Levi Spectre - 2010 - 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 (...)
  35. added 2018-10-01
    Conditionalization and Belief De Se.Darren Bradley - 2010 - Dialectica 64 (2):247-250.
    Colin Howson (1995 ) offers a counter-example to the rule of conditionalization. I will argue that the counter-example doesn't hit its target. The problem is that Howson mis-describes the total evidence the agent has. In particular, Howson overlooks how the restriction that the agent learn 'E and nothing else' interacts with the de se evidence 'I have learnt E'.
  36. added 2018-10-01
    Distorted Reflection.Rachael Briggs - 2009 - Philosophical Review 118 (1):59-85.
    Diachronic Dutch book arguments seem to support both conditionalization and Bas van Fraassen's Reflection principle. But the Reflection principle is vulnerable to numerous counterexamples. This essay addresses two questions: first, under what circumstances should an agent obey Reflection, and second, should the counterexamples to Reflection make us doubt the Dutch book for conditionalization? In response to the first question, this essay formulates a new "Qualified Reflection" principle, which states that an agent should obey Reflection only if he or she is (...)
  37. added 2018-10-01
    Is the Second-Step Conditionalization Unnecessary?In-mao Liu - 2009 - Behavioral and Brain Sciences 32 (1):92-93.
    Because the addition of the conditional premise tends to increase modus ponens (MP) inferences, Oaksford & Chater argue that the additional knowledge is assimilated to world knowledge before the Ramsey test is carried out to evaluate P(q|p), so that the process of applying the Ramsey test could become indistinguishable from the process of applying the second-step conditionalization.
  38. added 2018-10-01
    Bayesianism And Self-Locating Beliefs.Darren Bradley - 2007 - Dissertation, Stanford University
    How should we update our beliefs when we learn new evidence? Bayesian confirmation theory provides a widely accepted and well understood answer – we should conditionalize. But this theory has a problem with self-locating beliefs, beliefs that tell you where you are in the world, as opposed to what the world is like. To see the problem, consider your current belief that it is January. You might be absolutely, 100%, sure that it is January. But you will soon believe it (...)
  39. added 2018-10-01
    Beliefs in Conditionals Vs. Conditional Beliefs.Hannes Leitgeb - 2007 - Topoi 26 (1):115-132.
    On the basis of impossibility results on probability, belief revision, and conditionals, it is argued that conditional beliefs differ from beliefs in conditionals qua mental states. Once this is established, it will be pointed out in what sense conditional beliefs are still conditional, even though they may lack conditional contents, and why it is permissible to still regard them as beliefs, although they are not beliefs in conditionals. Along the way, the main logical, dispositional, representational, and normative properties of conditional (...)
  40. added 2018-10-01
    Conditionalization, Reflection, and Self-Knowledge.Jonathan Weisberg - 2007 - Philosophical Studies 135 (2):179-197.
    Van Fraassen famously endorses the Principle of Reflection as a constraint on rational credence, and argues that Reflection is entailed by the more traditional principle of Conditionalization. He draws two morals from this alleged entailment. First, that Reflection can be regarded as an alternative to Conditionalization – a more lenient standard of rationality. And second, that commitment to Conditionalization can be turned into support for Reflection. Van Fraassen also argues that Reflection implies Conditionalization, thus offering a new justification for Conditionalization. (...)
  41. added 2018-10-01
    Conditionalization Without Reflection.Jonathan Weisberg - 2005
    Conditionalization is an intuitive and popular epistemic principle. By contrast, the Reflection principle is well known to have some very unappealing consequences. But van Fraassen argues that Conditionalization entails Reflection, so that proponents of Conditionalization must accept Reflection and its consequences. Van Fraassen also argues that Reflection implies Conditionalization, thus offering a new justification for Conditionalization. I argue that neither principle entails the other, and thus neither can be used to motivate the other in the way van Fraassen says. I (...)
  42. added 2018-10-01
    Is Jeffrey Conditionalization Defective By Virtue of Being Non-Commutative? Remarks on the Sameness of Sensory Experiences.Marc Lange - 2000 - Synthese 123 (3):393-403.
  43. added 2018-10-01
    Calibration and the Epistemological Role of Bayesian Conditionalization.Marc Lange - 1999 - Journal of Philosophy 96 (6):294-324.
  44. added 2018-10-01
    The Coherence Argument Against Conditionalization.Matthias Hild - 1998 - Synthese 115 (2):229-258.
    I re-examine Coherence Arguments (Dutch Book Arguments, No Arbitrage Arguments) for diachronic constraints on Bayesian reasoning. I suggest to replace the usual game–theoretic coherence condition with a new decision–theoretic condition ('Diachronic Sure Thing Principle'). The new condition meets a large part of the standard objections against the Coherence Argument and frees it, in particular, from a commitment to additive utilities. It also facilitates the proof of the Converse Dutch Book Theorem. I first apply the improved Coherence Argument to van Fraassen's (...)
  45. added 2018-10-01
    Conditionalization, Cogency, and Cognitive Value.Graham Oddie - 1997 - British Journal for the Philosophy of Science 48 (4):533-541.
  46. added 2018-10-01
    Bayesian Conditionalization Resolves Positivist/Realist Disputes.Jon Dorling - 1992 - Journal of Philosophy 89 (7):362.
  47. added 2018-10-01
    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 any adequate (...)
  48. added 2018-10-01
    Bayesian Conditionalization Resolves Positivist/Realist Disputes.Jon Dorling - 1992 - Journal of Philosophy 89 (7):362-382.
  49. added 2018-10-01
    Against Conditionalization.Fahiem Bacchus, Henry E. Kyburg & Mariam Thalos - 1990 - Synthese 85 (3):475-506.
  50. added 2018-10-01
    Against Conditionalization.F. Bacchus, Mariam Thalos & H. E. Kyburg - 1990 - Synthese 85 (3):475 - 506.
1 — 50 / 324