About this topic
Summary

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 2002
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341 found
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1 — 50 / 341
  1. 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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  2. 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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  3. added 2020-02-07
    Reichenbach’s Posits Reposited.David Atkinson & Jeanne Peijnenburg - 2008 - 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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  4. added 2020-02-05
    Vague Credence.Aidan Lyon - 2017 - Synthese 194 (10):3931-3954.
    It is natural to think of precise probabilities as being special cases of imprecise probabilities, the special case being when one’s lower and upper probabilities are equal. I argue, however, that it is better to think of the two models as representing two different aspects of our credences, which are often vague to some degree. I show that by combining the two models into one model, and understanding that model as a model of vague credence, a natural interpretation arises that (...)
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  5. 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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  6. 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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  7. 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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  8. added 2019-11-08
    Commutativity, Normativity, and Holism: Lange Revisited.Lisa Cassell - 2020 - Canadian Journal of Philosophy 50 (2):159-173.
    Lange (2000) famously argues that although Jeffrey Conditionalization is non-commutative over evidence, it’s not defective in virtue of this feature. Since reversing the order of the evidence in a sequence of updates that don’t commute does not reverse the order of the experiences that underwrite these revisions, the conditions required to generate commutativity failure at the level of experience will fail to hold in cases where we get commutativity failure at the level of evidence. If our interest in commutativity is, (...)
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  9. added 2019-11-07
    Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
    Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...)
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  10. added 2019-11-07
    Learning From Conditionals.Benjamin Eva, Stephan Hartmann & Soroush Rafiee Rad - 2020 - Mind 129 (514):461-508.
    In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian norms is (...)
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  11. added 2019-11-07
    On Indeterminate Updating of Credences.Leendert Huisman - 2014 - Philosophy of Science 81 (4):537-557.
    The strategy of updating credences by minimizing the relative entropy has been questioned by many authors, most strongly by means of the Judy Benjamin puzzle. I present a new analysis of Judy Benjamin–like forms of new information and defend the thesis that in general the rational posterior is indeterminate, meaning that a family of posterior credence functions rather than a single one is the rational response when that type of information becomes available. The proposed thesis extends naturally to all cases (...)
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  12. added 2019-11-07
    Bayesian Probability.Patrick Maher - 2010 - Synthese 172 (1):119 - 127.
    Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant concept of rational choice. A satisfactory conception of Bayesian decision theory is obtained by taking Bayesian probability to be an explicatum for inductive probability given the agent’s evidence.
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  13. added 2019-11-07
    Bayesian Informal Logic and Fallacy.Kevin Korb - 2004 - Informal Logic 24 (1):41-70.
    Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.
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  14. added 2019-11-07
    Scientific Reasoning: The Bayesian Approach.Peter Urbach & Colin Howson - 1993 - 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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  15. added 2019-10-15
    Calibration and the Epistemological Role of Bayesian Conditionalization.Marc Lange - 1999 - Journal of Philosophy 96 (6):294-324.
  16. added 2019-10-10
    Time-Slice Rationality and Self-Locating Belief.David Builes - forthcoming - Philosophical Studies.
    The epistemology of self-locating belief concerns itself with how rational agents ought to respond to certain kinds of indexical information. I argue that those who endorse the thesis of Time-Slice Rationality ought to endorse a particular view about the epistemology of self-locating belief, according to which "essentially indexical" information is never evidentially relevant to non-indexical matters. I close by offering some independent motivations for endorsing Time-Slice Rationality in the context of the epistemology of self-locating belief.
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  17. added 2019-06-06
    The Ambiguity Aversion Literature: A Critical Assessment: Nabil I. Al-Najjar and Jonathan Weinstein.Nabil I. Al-Najjar - 2009 - Economics and Philosophy 25 (3):249-284.
    We provide a critical assessment of the ambiguity aversion literature, which we characterize in terms of the view that Ellsberg choices are rational responses to ambiguity, to be explained by relaxing Savage's Sure-Thing principle and adding an ambiguity-aversion postulate. First, admitting Ellsberg choices as rational leads to behaviour, such as sensitivity to irrelevant sunk cost, or aversion to information, which most economists would consider absurd or irrational. Second, we argue that the mathematical objects referred to as “beliefs” in the ambiguity (...)
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  18. added 2019-06-06
    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 (...)
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  19. added 2019-06-03
    Vasudevan on Judy Benjamin.Randall G. McCutcheon - manuscript
    Anubav Vasudevan characterized van Fraassen’s “Infomin” solution to the Judy Benjamin Problem (i.e. the solution by way of minimizing the Kullback-Leibler divergence between the posterior and prior) as an implementation of a “brand of epistemic charity” taking “the form of an assumption on the part of Judy Benjamin that her informant’s evidential report leaves out no relevant information”. After an analysis of the example that led Vasudevan to this way of thinking about Infomin, as well as of a new one (...)
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  20. 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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  21. 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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  22. 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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  23. 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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  24. added 2019-05-07
    Changing Minds in a Changing World.Wolfgang Schwarz - 2012 - Philosophical Studies 159 (2):219-239.
    I defend a general rule for updating beliefs that takes into account both the impact of new evidence and changes in the subject’s location. The rule combines standard conditioning with a shifting operation that moves the center of each doxastic possibility forward to the next point where information arrives. I show that well-known arguments for conditioning lead to this combination when centered information is taken into account. I also discuss how my proposal relates to other recent proposals, what results it (...)
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  25. added 2019-04-08
    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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  26. added 2019-04-08
    Belief Revision.Hans Rott - 1995 - In Jonathan Eric Adler & Lance J. Rips (eds.), Reasoning: Studies of Human Inference and its Foundations. Cambridge University Press. pp. 514--534.
    This is a survey paper. Contents: 1 Introduction -- 2 The representation of belief -- 3 Kinds of belief change -- 4 Coherence constraints for belief revision -- 5 Different modes of belief change -- 6 Two strategies for characterizing rational changes of belief - 6.1 The postulates strategy - 6.2 The constructive strategy -- 7 An abstract view of the elements of belief change -- 8 Iterated changes of belief -- 9 Further developments - 9.1 Variants and extensions of (...)
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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 2019-03-04
    Reaching a Consensus.Richard Bradley - unknown
    This paper explores some aspects of the relation between different ways of achieving a consensus on the judgemental values of a group of indviduals; in particular, aggregation and deliberation. We argue firstly that the framing of an aggregation problem itself generates information that individuals are rationally obliged to take into account. And secondly that outputs of the deliberative process that this initiates is in tension with constraints on consensual values typically imposed by aggregation theory, at least when deliberation is modelled (...)
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  29. 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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  30. 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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  31. 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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  32. 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 (...)
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  33. added 2018-11-05
    The Art of Learning.Jason Konek - forthcoming - Oxford Studies in Epistemology 7.
    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 (...)
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  34. 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 (...)
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  35. added 2018-11-05
    Higher-Order Beliefs and the Undermining Problem for Bayesianism.Lisa Cassell - 2019 - Acta Analytica 34 (2):197-213.
    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 (...)
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  36. added 2018-11-05
    Self‐Locating Evidence and the Metaphysics of Time.David Builes - 2019 - Philosophy and Phenomenological Research 99 (2):478-490.
    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 (...)
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  37. 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). (...)
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  38. 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 (...)
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  39. 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 (...)
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  40. 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 (...)
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  41. 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 (...)
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  42. added 2018-11-05
    Agreeing to Disagree in Probabilistic Dynamic Epistemic Logic.Lorenz6 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 (...)
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  43. 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 (...)
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  44. 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 (...)
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  45. 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 (...)
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  46. 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 (...)
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  47. 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 (...)
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  48. 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 (...)
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  49. 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 (...)
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  50. 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 (...)
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