Results for 'Bayesian updating'

784 found
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  1. Bayesian Updating When What You Learn Might Be False.Richard Pettigrew - forthcoming - Erkenntnis:1-16.
    Michael Rescorla (2020) has recently pointed out that the standard arguments for Bayesian Conditionalization assume that whenever you take yourself to learn something with certainty, it's true. Most people would reject this assumption. In response, Rescorla offers an improved Dutch Book argument for Bayesian Conditionalization that does not make this assumption. My purpose in this paper is two-fold. First, I want to illuminate Rescorla's new argument by giving a very general Dutch Book argument that applies to many cases (...)
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  2. Can Resources Save Rationality? ‘Anti-BayesianUpdating in Cognition and Perception.Eric Mandelbaum, Isabel Won, Steven Gross & Chaz Firestone - 2020 - Behavioral and Brain Sciences 143:e16.
    Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?
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  3. Bayesian Coherentism.Lisa Cassell - 2021 - Synthese 198 (10):9563-9590.
    This paper considers a problem for Bayesian epistemology and proposes a solution to it. On the traditional Bayesian framework, an agent updates her beliefs by Bayesian conditioning, a rule that tells her how to revise her beliefs whenever she gets evidence that she holds with certainty. In order to extend the framework to a wider range of cases, Jeffrey (1965) proposed a more liberal version of this rule that has Bayesian conditioning as a special case. Jeffrey (...)
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  4.  39
    Time-Slice Epistemology for Bayesians.Lisa Cassell - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    Recently, some have challenged the idea that there are genuine norms of diachronic rationality. Part of this challenge has involved offering replacements for diachronic principles. Skeptics about diachronic rationality believe that we can provide an error theory for it by appealing to synchronic updating rules that, over time, mimic the behavior of diachronic norms. In this paper, I argue that the most promising attempts to develop this position within the Bayesian framework are unsuccessful. I sketch a new synchronic (...)
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  5.  88
    Accurate Updating.Ginger Schultheis - manuscript
    Accuracy-first epistemology says that the rational update rule is the rule that maximizes expected accuracy. Externalism says, roughly, that we do not always know what our total evidence is. It’s been argued in recent years that the externalist faces a dilemma: Either deny that Bayesian Conditionalization is the rational update rule, thereby rejecting traditional Bayesian epistemology, or else deny that the rational update rule is the rule that maximizes expected accuracy, thereby rejecting the accuracy-first program. Call this the (...)
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  6. Updating for Externalists.J. Dmitri Gallow - 2021 - Noûs 55 (3):487-516.
    The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In (...)
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  7. The Bayesian and the Dogmatist.Brian Weatherson - 2007 - Proceedings of the Aristotelian Society 107 (1pt2):169-185.
    Dogmatism is sometimes thought to be incompatible with Bayesian models of rational learning. I show that the best model for updating imprecise credences is compatible with dogmatism.
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  8. Updating on the Credences of Others: Disagreement, Agreement, and Synergy.Kenny Easwaran, Luke Fenton-Glynn, Christopher Hitchcock & Joel D. Velasco - 2016 - Philosophers’ Imprint 16:1--39.
    We introduce a family of rules for adjusting one's credences in response to learning the credences of others. These rules have a number of desirable features. 1. They yield the posterior credences that would result from updating by standard Bayesian conditionalization on one's peers' reported credences if one's likelihood function takes a particular simple form. 2. In the simplest form, they are symmetric among the agents in the group. 3. They map neatly onto the familiar Condorcet voting results. (...)
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  9. A Bayesian Analysis of Debunking Arguments in Ethics.Shang Long Yeo - 2021 - Philosophical Studies 179 (5):1673-1692.
    Debunking arguments in ethics contend that our moral beliefs have dubious evolutionary, cultural, or psychological origins—hence concluding that we should doubt such beliefs. Debates about debunking are often couched in coarse-grained terms—about whether our moral beliefs are justified or not, for instance. In this paper, I propose a more detailed Bayesian analysis of debunking arguments, which proceeds in the fine-grained framework of rational confidence. Such analysis promises several payoffs: it highlights how debunking arguments don’t affect all agents, but rather (...)
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  10. Bayesian Beauty.Silvia Milano - 2022 - Erkenntnis 87 (2):657-676.
    The Sleeping Beauty problem has attracted considerable attention in the literature as a paradigmatic example of how self-locating uncertainty creates problems for the Bayesian principles of Conditionalization and Reflection. Furthermore, it is also thought to raise serious issues for diachronic Dutch Book arguments. I show that, contrary to what is commonly accepted, it is possible to represent the Sleeping Beauty problem within a standard Bayesian framework. Once the problem is correctly represented, the ‘thirder’ solution satisfies standard rationality principles, (...)
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  11. Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity.Patrick Grim, Frank Seidl, Calum McNamara, Hinton E. Rago, Isabell N. Astor, Caroline Diaso & Peter Ryner - 2022 - Philosophy of Science 89 (1):42-69.
    We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to (...)
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  12. Fully Bayesian Aggregation.Franz Dietrich - forthcoming - Journal of Economic Theory.
    Can a group be an orthodox rational agent? This requires the group's aggregate preferences to follow expected utility (static rationality) and to evolve by Bayesian updating (dynamic rationality). Group rationality is possible, but the only preference aggregation rules which achieve it (and are minimally Paretian and continuous) are the linear-geometric rules, which combine individual values linearly and combine individual beliefs geometrically. Linear-geometric preference aggregation contrasts with classic linear-linear preference aggregation, which combines both values and beliefs linearly, but achieves (...)
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  13. Updating Incoherent Credences - Extending the Dutch Strategy Argument for Conditionalization.Glauber De Bona & Julia Staffel - forthcoming - Philosophy and Phenomenological Research.
    In this paper, we ask: how should an agent who has incoherent credences update when they learn new evidence? The standard Bayesian answer for coherent agents is that they should conditionalize; however, this updating rule is not defined for incoherent starting credences. We show how one of the main arguments for conditionalization, the Dutch strategy argument, can be extended to devise a target property for updating plans that can apply to them regardless of whether the agent starts (...)
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  14.  46
    When the (Bayesian) Ideal is Not Ideal.Danilo Dantas - manuscript
    Bayesian epistemologists propose norms of rationality based on the probability calculus. ?Probabilism? states that agents must hold credences that are consistent with the axioms of probability. ?Conditionalization? states that credences must be updated using Bayesian conditionalization. These norms are supported using `maximization arguments' such as Dutch book and accuracy arguments. These arguments presuppose that rationality requires agents to maximize (practical or epistemic) value in every doxastic state, whose evaluation is done from a subjective point of view. Accuracy arguments (...)
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  15. Updating, Undermining, and Perceptual Learning.Brian Miller - 2017 - Philosophical Studies 174 (9):2187-2209.
    As I head home from work, I’m not sure whether my daughter’s new bike is green, and I’m also not sure whether I’m on drugs that distort my color perception. One thing that I am sure about is that my attitudes towards those possibilities are evidentially independent of one another, in the sense that changing my confidence in one shouldn’t affect my confidence in the other. When I get home and see the bike it looks green, so I increase my (...)
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  16. Believing Conspiracy Theories: A Bayesian Approach to Belief Protection.Nina Poth & Krzysztof Dolega - manuscript
    Despite the harmful impact of conspiracy theories on the public discourse, there is little agreement about their exact nature. Rather than define conspiracy theories as such, we focus on the notion of conspiracy belief. We analyse three recent proposals that identify belief in conspiracy theories as an effect of irrational reasoning. Although these views are sometimes presented as competing alternatives, they share the main commitment that conspiracy beliefs are epistemically flawed because they resist revision given disconfirming evidence. However, the three (...)
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  17. Classical Versus Bayesian Statistics.Eric Johannesson - 2020 - Philosophy of Science 87 (2):302-318.
    In statistics, there are two main paradigms: classical and Bayesian statistics. The purpose of this article is to investigate the extent to which classicists and Bayesians can agree. My conclusion is that, in certain situations, they cannot. The upshot is that, if we assume that the classicist is not allowed to have a higher degree of belief in a null hypothesis after he has rejected it than before, then he has to either have trivial or incoherent credences to begin (...)
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  18. A Dual Approach to Bayesian Inference and Adaptive Control.Leigh Tesfatsion - 1982 - Theory and Decision 14 (2):177-194.
    Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.
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  19.  7
    When the (Bayesian) Ideal is Not Ideal.Danilo Fraga Dantas - manuscript
    Bayesian epistemologists propose norms of rationality based on the proba- bility calculus. ?Probabilism? states that agents must hold credences that are consistent with the axioms of probability. ?Conditionalization? states that credences must be updated using Bayesian conditionalization. These norms are supported using `maximization arguments' such as Dutch book and accuracy arguments. These arguments presuppose that rationality requires agents to maximize (practical or epistemic) value in every doxastic state, whose evaluation is done from a subjective point of view. Accuracy (...)
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  20.  85
    Comments on Carl Wagner's Jeffrey Conditioning and External Bayesianity.Steve Petersen - manuscript
    Jeffrey conditioning allows updating in Bayesian style when the evidence is uncertain. A weighted average, essentially, over classically updating on the alternatives. Unlike classical Bayesian conditioning, this allows learning to be unlearned.
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  21. Arguments From Expert Opinion – An Epistemological Approach.Christoph Lumer - 2020 - In Catarina Dutilh Novaes, Henrike Jansen, Jan Albert Van Laar & Bart Verheij (eds.), Reason to Dissent. Proceedings of the 3rd European Conference on Argumentation. College Publications. pp. 403-422.
    In times of populist mistrust towards experts, it is important and the aim of the paper to ascertain the rationality of arguments from expert opinion and to reconstruct their rational foundations as well as to determine their limits. The foundational approach chosen is probabilistic. However, there are at least three correct probabilistic reconstructions of such argumentations: statistical inferences, Bayesian updating, and interpretive arguments. To solve this competition problem, the paper proposes a recourse to the arguments' justification strengths achievable (...)
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  22. Logical Ignorance and Logical Learning.Richard Pettigrew - 2021 - Synthese 198 (10):9991-10020.
    According to certain normative theories in epistemology, rationality requires us to be logically omniscient. Yet this prescription clashes with our ordinary judgments of rationality. How should we resolve this tension? In this paper, I focus particularly on the logical omniscience requirement in Bayesian epistemology. Building on a key insight by Hacking :311–325, 1967), I develop a version of Bayesianism that permits logical ignorance. This includes: an account of the synchronic norms that govern a logically ignorant individual at any given (...)
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  23. Inference to the Best Explanation Made Incoherent.Nevin Climenhaga - 2017 - Journal of Philosophy 114 (5):251-273.
    Defenders of Inference to the Best Explanation claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, as well as how a rule for degrees of belief should relate to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE. My argument focuses (...)
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  24. What is conditionalization, and why should we do it?Richard Pettigrew - 2020 - Philosophical Studies 177 (11):3427-3463.
    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 (...)
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  25. Approximate Coherentism and Luck.Boris Babic - 2021 - Philosophy of Science 88 (4):707-725.
    Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent (...)
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  26. On the Pragmatic and Epistemic Virtues of Inference to the Best Explanation.Richard Pettigrew - 2021 - Synthese 199 (5-6):12407-12438.
    In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation that cannot be accommodated within their framework. In this paper, I survey their worries and attempt to answer them using a series of pragmatic and purely epistemic arguments that I take to show that Bayes’ Rule really is the only rational way to respond to your evidence.
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  27.  99
    Deliberation and confidence change.Nora Heinzelmann & Stephan Hartmann - 2022 - Synthese 200 (1):1-13.
    We argue that social deliberation may increase an agent’s confidence and credence under certain circumstances. An agent considers a proposition H and assigns a probability to it. However, she is not fully confident that she herself is reliable in this assignment. She then endorses H during deliberation with another person, expecting him to raise serious objections. To her surprise, however, the other person does not raise any objections to H. How should her attitudes toward H change? It seems plausible that (...)
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  28. Troubles with Bayesianism: An Introduction to the Psychological Immune System.Eric Mandelbaum - 2019 - Mind and Language 34 (2):141-157.
    A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.
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  29. Learning Not to Be Naïve: A Comment on the Exchange Between Perrine/Wykstra and Draper.Lara Buchak - 2014 - In Trent Dougherty & Justin McBrayer (eds.), Skeptical Theism: New Essays. Oxford University Press.
    Does postulating skeptical theism undermine the claim that evil strongly confirms atheism over theism? According to Perrine and Wykstra, it does undermine the claim, because evil is no more likely on atheism than on skeptical theism. According to Draper, it does not undermine the claim, because evil is much more likely on atheism than on theism in general. I show that the probability facts alone do not resolve their disagreement, which ultimately rests on which updating procedure – conditionalizing or (...)
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  30. Demystifying Dilation.Arthur Paul Pedersen & Gregory Wheeler - 2014 - Erkenntnis 79 (6):1305-1342.
    Dilation occurs when an interval probability estimate of some event E is properly included in the interval probability estimate of E conditional on every event F of some partition, which means that one’s initial estimate of E becomes less precise no matter how an experiment turns out. Critics maintain that dilation is a pathological feature of imprecise probability models, while others have thought the problem is with Bayesian updating. However, two points are often overlooked: (1) knowing that E (...)
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  31. Strength of Justification – The Rational Degree of Certainty Approach.Christoph Lumer - 2018 - In Steve Oswald (ed.), Argumentation and Inference. Proceedings of the 2nd European Conference on Argumentation, Fribourg 2017. London, GB: College Publications. pp. 315-333.
    In this paper, I present the fundamental ideas of a new theory of justification strength. This theory is based on the epistemological approach to argumentation. Even the thesis of a valid justification can be false for various reasons. The theory outlined here identifies such possible errors. Justification strength is equated with the degree to which such possible errors are excluded. The natural expression of this kind of justification strength is the (rational) degree of certainty of the belief in the thesis.
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  32. Defeasible Conditionalization.Paul D. Thorn - 2014 - Journal of Philosophical Logic 43 (2-3):283-302.
    The applicability of Bayesian conditionalization in setting one’s posterior probability for a proposition, α, is limited to cases where the value of a corresponding prior probability, PPRI(α|∧E), is available, where ∧E represents one’s complete body of evidence. In order to extend probability updating to cases where the prior probabilities needed for Bayesian conditionalization are unavailable, I introduce an inference schema, defeasible conditionalization, which allows one to update one’s personal probability in a proposition by conditioning on a proposition (...)
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  33. Plenty of Room Left for the Dogmatist.Thomas Raleigh - 2020 - Analysis 80 (1):66-76.
    Barnett provides an interesting new challenge for Dogmatist accounts of perceptual justification. The challenge is that such accounts, by accepting that a perceptual experience can provide a distinctive kind of boost to one’s credences, would lead to a form of diachronic irrationality in cases where one has already learnt in advance that one will have such an experience. I show that this challenge rests on a misleading feature of using the 0–1 interval to express probabilities and show that if we (...)
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  34. 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 (...)
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  35. Reply to Crupi Et Al.'s ‘Confirmation by Uncertain Evidence’.Franz Huber - 2008 - British Journal for the Philosophy of Science 59 (2):213-215.
    Crupi et al. 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 at t0 to . . . to Pri at ti to . . . to Prn at tn. It is understood that the agent’s subjective probabilities change (...)
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  36. Ur-Priors, Conditionalization, and Ur-Prior Conditionalization.Christopher J. G. Meacham - 2016 - Ergo: An Open Access Journal of Philosophy 3.
    Conditionalization is a widely endorsed rule for updating one’s beliefs. But a sea of complaints have been raised about it, including worries regarding how the rule handles error correction, changing desiderata of theory choice, evidence loss, self-locating beliefs, learning about new theories, and confirmation. In light of such worries, a number of authors have suggested replacing Conditionalization with a different rule — one that appeals to what I’ll call “ur-priors”. But different authors have understood the rule in different ways, (...)
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  37. Higher-Order Evidence and the Dynamics of Self-Location: An Accuracy-Based Argument for Calibrationism.Brett Topey - forthcoming - Erkenntnis:1-27.
    The thesis that agents should calibrate their beliefs in the face of higher-order evidence—i.e., should adjust their first-order beliefs in response to evidence suggesting that the reasoning underlying those beliefs is faulty—is sometimes thought to be in tension with Bayesian approaches to belief update: in order to obey Bayesian norms, it's claimed, agents must remain steadfast in the face of higher-order evidence. But I argue that this claim is incorrect. In particular, I motivate a minimal constraint on a (...)
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  38. Rational Polarization.Kevin Dorst - manuscript
    Predictable polarization is everywhere: we can often predict how people’s opinions—including our own—will shift over time. Extant theories either neglect the fact that we can predict our own polarization, or explain it through irrational mechanisms. We needn’t. Empirical studies suggest that polarization is predictable when evidence is ambiguous, i.e. when the rational response is not obvious. I show how Bayesians should model such ambiguity, and then prove that—assuming rational updates are those which obey the value of evidence (Blackwell 1953; Good (...)
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  39. Groupthink.Jeffrey Sanford Russell, John Hawthorne & Lara Buchak - 2015 - Philosophical Studies 172 (5):1287-1309.
    How should a group with different opinions (but the same values) make decisions? In a Bayesian setting, the natural question is how to aggregate credences: how to use a single credence function to naturally represent a collection of different credence functions. An extension of the standard Dutch-book arguments that apply to individual decision-makers recommends that group credences should be updated by conditionalization. This imposes a constraint on what aggregation rules can be like. Taking conditionalization as a basic constraint, we (...)
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  40. Pooling: A User's Guide.Richard Pettigrew & Jonathan Weisberg - manuscript
    We often learn the credences of others without getting to hear the evidence on which they’re based. And, in these cases, it is often unfeasible or overly onerous to update on this social evidence by conditionalizing on it. How, then, should we respond to it? We consider four methods for aggregating your credences with the credences of others: arithmetic, geometric, multiplicative, and harmonic pooling. Each performs well for some purposes and poorly for others. We describe these in Sections 1-4. In (...)
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  41. 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 (...)
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  42.  7
    Epistemic Modality and Coordination Under Uncertainty.Giorgio Sbardolini - 2021 - Proceedings of TARK 355:295--306.
    Communication facilitates coordination, but coordination might fail if there's too much uncertainty. I discuss a scenario in which vagueness-driven uncertainty undermines the possibility of publicly sharing a belief. I then show that asserting an epistemic modal sentence, 'Might p', can reveal the speaker's uncertainty, and that this may improve the chances of coordination despite the lack of a common epistemic ground. This provides a game-theoretic rationale for epistemic modality. The account draws on a standard relational semantics for epistemic modality, Stalnaker's (...)
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  43. Unravelling the Tangled Web: Continuity, Internalism, Non-Uniqueness and Self-Locating Beliefs.Christopher J. G. Meacham - 2010 - In Tamar Szabo Gendler & John Hawthorne (eds.), Oxford Studies in Epistemology Volume 3. Oxford University Press. pp. 86.
    A number of cases involving self-locating beliefs have been discussed in the Bayesian literature. I suggest that many of these cases, such as the sleeping beauty case, are entangled with issues that are independent of self-locating beliefs per se. In light of this, I propose a division of labor: we should address each of these issues separately before we try to provide a comprehensive account of belief updating. By way of example, I sketch some ways of extending Bayesianism (...)
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  44. 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. (...)
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  45. Another Approach to Consensus and Maximally Informed Opinions with Increasing Evidence.Rush T. Stewart & Michael Nielsen - 2018 - Philosophy of Science (2):236-254.
    Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Initial subjectivity, the line goes, is of mere transient significance, giving way to intersubjective agreement eventually. Here, we establish a merging result for sets of probability measures that are updated by Jeffrey conditioning. This generalizes a number of different merging results in the (...)
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  46.  98
    Machine-Believers Learning Faiths & Knowledges: The New Gospel of Artificial Intelligence.Virgil W. Brower - 2021 - Internationales Jahrbuch Für Medienphilosophie 7 (1):97-121.
    One is occasionally reminded of Foucault's proclamation in a 1970 interview that "perhaps, one day this century will be known as Deleuzian." Less often is one compelled to update and restart with a supplementary counter-proclamation of the mathematician, David Lindley: "the twenty-first century would be a Bayesian era..." The verb tenses of both are conspicuous. // To critically attend to what is today often feared and demonized, but also revered, deployed, and commonly referred to as algorithm(s), one cannot avoid (...)
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  47. Not So Phenomenal!Maria Lasonen-Aarnio & John Hawthorne - forthcoming - The Philosophical Review.
    Our main aims in this paper is to discuss and criticise the core thesis of a position that has become known as phenomenal conservatism. According to this thesis, its seeming to one that p provides enough justification for a belief in p to be prima facie justified (a thesis we label Standard Phenomenal Conservatism). This thesis captures the special kind of epistemic import that seemings are claimed to have. To get clearer on this thesis, we embed it, first, in a (...)
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  48. Update rules and semantic universals.Luca Incurvati & Giorgio Sbardolini - forthcoming - Linguistics and Philosophy:1-31.
    We discuss a well-known puzzle about the lexicalization of logical operators in natural language, in particular connectives and quantifiers. Of the many logically possible operators, only few appear in the lexicon of natural languages: the connectives in English, for example, are conjunction and, disjunction or, and negated disjunction nor; the lexical quantifiers are all, some and no. The logically possible nand and Nall are not expressed by lexical entries in English, nor in any natural language. Moreover, the lexicalized operators are (...)
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  49. Updating Without Evidence.Yoaav Isaacs & Jeffrey Sanford Russell - forthcoming - Noûs.
    Sometimes you are unreliable at fulfilling your doxastic plans: for example, if you plan to be fully confident in all truths, probably you will end up being fully confident in some falsehoods by mistake. In some cases, there is information that plays the classical role of *evidence*—your beliefs are perfectly discriminating with respect to some possible facts about the world—and there is a standard expected-accuracy-based justification for planning to *conditionalize* on this evidence. This planning-oriented justification extends to some cases where (...)
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  50. Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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