Results for 'Bayesianism'

112 found
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  1. 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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  2. Bayesianism for Non-Ideal Agents.Mattias Skipper & Jens Christian Bjerring - forthcoming - Erkenntnis:1-23.
    Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience (...)
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  3. Imprecise Bayesianism and Global Belief Inertia.Aron Vallinder - 2018 - British Journal for the Philosophy of Science 69 (4):1205-1230.
    Traditional Bayesianism requires that an agent’s degrees of belief be represented by a real-valued, probabilistic credence function. However, in many cases it seems that our evidence is not rich enough to warrant such precision. In light of this, some have proposed that we instead represent an agent’s degrees of belief as a set of credence functions. This way, we can respect the evidence by requiring that the set, often called the agent’s credal state, includes all credence functions that are (...)
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  4. 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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  5. Bayesianism, Convergence and Social Epistemology.Michael J. Shaffer - 2008 - Episteme 5 (2):pp. 203-219.
    Following the standard practice in sociology, cultural anthropology and history, sociologists, historians of science and some philosophers of science define scientific communities as groups with shared beliefs, values and practices. In this paper it is argued that in real cases the beliefs of the members of such communities often vary significantly in important ways. This has rather dire implications for the convergence defense against the charge of the excessive subjectivity of subjective Bayesianism because that defense requires that communities of (...)
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  6. 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 (...)
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  7. The Epistemology of Disagreement: Why Not Bayesianism?Thomas Mulligan - forthcoming - Episteme.
    Disagreement is a ubiquitous feature of human life, and philosophers have dutifully attended to it. One important question related to disagreement is epistemological: How does a rational person change her beliefs (if at all) in light of disagreement from others? The typical methodology for answering this question is to endorse a steadfast or conciliatory disagreement norm (and not both) on a priori grounds and selected intuitive cases. In this paper, I argue that this methodology is misguided. Instead, a thoroughgoingly Bayesian (...)
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  8. 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.
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  9. How to Be a Bayesian Dogmatist.Brian T. Miller - 2016 - Australasian Journal of Philosophy 94 (4):766-780.
    ABSTRACTRational agents have consistent beliefs. Bayesianism is a theory of consistency for partial belief states. Rational agents also respond appropriately to experience. Dogmatism is a theory of how to respond appropriately to experience. Hence, Dogmatism and Bayesianism are theories of two very different aspects of rationality. It's surprising, then, that in recent years it has become common to claim that Dogmatism and Bayesianism are jointly inconsistent: how can two independently consistent theories with distinct subject matter be jointly (...)
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  10. Bayesian Orgulity.Gordon Belot - 2013 - Philosophy of Science 80 (4):483-503.
    A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents that, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the present discussion are to show that kernel of truth in this folklore is in some ways fairly small and to argue that Bayesian convergence-to-the-truth results are a liability for Bayesianism as an account of rationality, since they render a certain sort (...)
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  11. A Theory of Bayesian Groups.Franz Dietrich - 2019 - Noûs 53 (3):708-736.
    A group is often construed as one agent with its own probabilistic beliefs (credences), which are obtained by aggregating those of the individuals, for instance through averaging. In their celebrated “Groupthink”, Russell et al. (2015) require group credences to undergo Bayesian revision whenever new information is learnt, i.e., whenever individual credences undergo Bayesian revision based on this information. To obtain a fully Bayesian group, one should often extend this requirement to non-public or even private information (learnt by not all or (...)
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  12. Chalmers on the Objects of Credence.Jesse Fitts - 2014 - Philosophical Studies 170 (2):343-358.
    Chalmers (Mind 120(479): 587–636, 2011a) presents an argument against “referentialism” (and for his own view) that employs Bayesianism. He aims to make progress in a debate over the objects of belief, which seems to be at a standstill between referentialists and non-referentialists. Chalmers’ argument, in sketch, is that Bayesianism is incompatible with referentialism, and natural attempts to salvage the theory, Chalmers contends, requires giving up referentialism. Given the power and success of Bayesianism, the incompatibility is prima facie (...)
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  13. Understanding Conditionalization.Christopher J. G. Meacham - 2015 - Canadian Journal of Philosophy 45 (5):767-797.
    At the heart of the Bayesianism is a rule, Conditionalization, which tells us how to update our beliefs. Typical formulations of this rule are underspecified. This paper considers how, exactly, this rule should be formulated. It focuses on three issues: when a subject’s evidence is received, whether the rule prescribes sequential or interval updates, and whether the rule is narrow or wide scope. After examining these issues, it argues that there are two distinct and equally viable versions of Conditionalization (...)
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  14. Can There Be a Bayesian Explanationism? On the Prospects of a Productive Partnership.Frank Cabrera - 2017 - Synthese 194 (4):1245–1272.
    In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing (...)
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  15. A Bayesian Explanation of the Irrationality of Sexist and Racist Beliefs Involving Generic Content.Paul Silva - 2020 - Synthese 197 (6):2465-2487.
    Various sexist and racist beliefs ascribe certain negative qualities to people of a given sex or race. Epistemic allies are people who think that in normal circumstances rationality requires the rejection of such sexist and racist beliefs upon learning of many counter-instances, i.e. members of these groups who lack the target negative quality. Accordingly, epistemic allies think that those who give up their sexist or racist beliefs in such circumstances are rationally responding to their evidence, while those who do not (...)
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  16. Failure of Calibration is Typical.Gordon Belot - 2013 - Statistics and Probability Letters 83:2316--2318.
    Schervish (1985b) showed that every forecasting system is noncalibrated for uncountably many data sequences that it might see. This result is strengthened here: from a topological point of view, failure of calibration is typical and calibration rare. Meanwhile, Bayesian forecasters are certain that they are calibrated---this invites worries about the connection between Bayesianism and rationality.
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  17. The Heuristic Conception of Inference to the Best Explanation.Finnur Dellsén - 2018 - Philosophical Studies 175 (7):1745-1766.
    An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a (...)
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  18.  18
    Awareness Growth and Dispositional Attitudes.Anna Mahtani - forthcoming - Synthese:1-17.
    Richard Bradley and others endorse Reverse Bayesianism as the way to model awareness growth. I raise a problem for Reverse Bayesianism—at least for the general version that Bradley endorses—and argue that there is no plausible way to restrict the principle that will give us the right results. To get the right results, we need to pay attention to the attitudes that agents have towards propositions of which they are unaware. This raises more general questions about how awareness growth (...)
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  19. Evidential Probabilities and Credences.Anna-Maria Eder - 2019 - British Journal for the Philosophy of Science:1-21.
    Enjoying great popularity in decision theory, epistemology, and philosophy of science, Bayesianism as understood here is fundamentally concerned with epistemically ideal rationality. It assumes a tight connection between evidential probability and ideally rational credence, and usually interprets evidential probability in terms of such credence. Timothy Williamson challenges Bayesianism by arguing that evidential probabilities cannot be adequately interpreted as the credences of an ideal agent. From this and his assumption that evidential probabilities cannot be interpreted as the actual credences (...)
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  20. The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science.Matthias Unterhuber & Gerhard Schurz - 2013 - Synthese 190 (8):1407-1435.
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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  21. Evidence of Evidence as Higher Order Evidence.Anna-Maria A. Eder & Peter Brössel - 2019 - In Mattias Skipper & Asbjørn Steglich-Petersen (eds.), Higher-Order Evidence: New Essays. Oxford University Press. pp. 62-83.
    In everyday life and in science we acquire evidence of evidence and based on this new evidence we often change our epistemic states. An assumption underlying such practice is that the following EEE Slogan is correct: 'evidence of evidence is evidence' (Feldman 2007, p. 208). We suggest that evidence of evidence is best understood as higher-order evidence about the epistemic state of agents. In order to model evidence of evidence we introduce a new powerful framework for modelling epistemic states, Dyadic (...)
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  22.  53
    Support for Geometric Pooling.Jean Baccelli & Rush T. Stewart - forthcoming - Review of Symbolic Logic:1-40.
    Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be non-trivially Bayes-compatible. We show by contrast that geometric pooling can be non-trivially Bayes-compatible. Indeed, we show that, under certain assumptions, geometric (...)
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  23. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 2010 - In DancyJ (ed.), A Companion to Epistemology. Blackwell.
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a Bayesian epistemology?
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  24.  70
    Review of Ted Poston's Reason and Explanation: A Defense of Explanatory Coherentism (2014, Palgrave Macmillan). [REVIEW]Roche William - 2015 - Notre Dame Philosophical Reviews:1-7.
    Ted Poston's book Reason and Explanation: A Defense of Explanatory Coherentism is a book worthy of careful study. Poston develops and defends an explanationist theory of (epistemic) justification on which justification is a matter of explanatory coherence which in turn is a matter of conservativeness, explanatory power, and simplicity. He argues that his theory is consistent with Bayesianism. He argues, moreover, that his theory is needed as a supplement to Bayesianism. There are seven chapters. I provide a chapter-by-chapter (...)
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  25. O Problema da Indução.Eduardo Castro & Diogo Fernandes - 2014 - Compêndio Em Linha de Problemas de Filosofia Analítica.
    State of the art paper on the problem of induction: how to justify the conclusion that ‘all Fs are Gs’ from the premise that ‘all observed Fs are Gs’. The most prominent theories of contemporary philosophical literature are discussed and analysed, such as: inductivism, reliabilism, perspective of laws of nature, rationalism, falsificationism, the material theory of induction and probabilistic approaches, according to Carnap, Reichenbach and Bayesianism. In the end, we discuss the new problem of induction of Goodman, raised by (...)
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  26. Subjective Probabilities as Basis for Scientific Reasoning?Franz Huber - 2005 - British Journal for the Philosophy of Science 56 (1):101-116.
    Bayesianism is the position that scientific reasoning is probabilistic and that probabilities are adequately interpreted as an agent's actual subjective degrees of belief, measured by her betting behaviour. Confirmation is one important aspect of scientific reasoning. The thesis of this paper is the following: if scientific reasoning is at all probabilistic, the subjective interpretation has to be given up in order to get right confirmation—and thus scientific reasoning in general. The Bayesian approach to scientific reasoning Bayesian confirmation theory The (...)
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  27. Binding and its Consequences.Christopher J. G. Meacham - 2010 - Philosophical Studies 149 (1):49-71.
    In “Bayesianism, Infinite Decisions, and Binding”, Arntzenius et al. (Mind 113:251–283, 2004 ) present cases in which agents who cannot bind themselves are driven by standard decision theory to choose sequences of actions with disastrous consequences. They defend standard decision theory by arguing that if a decision rule leads agents to disaster only when they cannot bind themselves, this should not be taken to be a mark against the decision rule. I show that this claim has surprising implications for (...)
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  28. How Explanation Guides Confirmation.Nevin Climenhaga - 2017 - Philosophy of Science 84 (2):359-68.
    Where E is the proposition that [If H and O were true, H would explain O], William Roche and Elliot Sober have argued that P(H|O&E) = P(H|O). In this paper I argue that not only is this equality not generally true, it is false in the very kinds of cases that Roche and Sober focus on, involving frequency data. In fact, in such cases O raises the probability of H only given that there is an explanatory connection between them.
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  29. Rational Credence Through Reasoning.Sinan Dogramaci - 2018 - Philosophers' Imprint 18.
    Whereas Bayesians have proposed norms such as probabilism, which requires immediate and permanent certainty in all logical truths, I propose a framework on which credences, including credences in logical truths, are rational because they are based on reasoning that follows plausible rules for the adoption of credences. I argue that my proposed framework has many virtues. In particular, it resolves the problem of logical omniscience.
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  30. Can Resources Save Rationality? ‘Anti-Bayesian’ Updating 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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  31. Is QBism the Future of Quantum Physics? [REVIEW]Kelvin McQueen - 2017 - Quantum Times 2017.
    The purpose of this book is to explain Quantum Bayesianism (‘QBism’) to “people without easy access to mathematical formulas and equations” (4-5). Qbism is an interpretation of quantum mechanics that “doesn’t meddle with the technical aspects of the theory [but instead] reinterprets the fundamental terms of the theory and gives them new meaning” (3). The most important motivation for QBism, enthusiastically stated on the book’s cover, is that QBism provides “a way past quantum theory’s paradoxes and puzzles” such that (...)
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  32. Holistic Conditionalization and Underminable Perceptual Learning.Brian T. Miller - 2020 - Philosophy and Phenomenological Research 101 (1):130-149.
    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. Dutch Books, Additivity, and Utility Theory.Brad Armendt - 1993 - Philosophical Topics 21 (1):1-20.
    One guide to an argument's significance is the number and variety of refutations it attracts. By this measure, the Dutch book argument has considerable importance.2 Of course this measure alone is not a sure guide to locating arguments deserving of our attention—if a decisive refutation has really been given, we are better off pursuing other topics. But the presence of many and varied counterarguments at least suggests that either the refutations are controversial, or that their target admits of more than (...)
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  34. Is Explanatoriness a Guide to Confirmation? A Reply to Climenhaga.William Roche & Elliott Sober - 2017 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 48 (4):581-590.
    We argued that explanatoriness is evidentially irrelevant in the following sense: Let H be a hypothesis, O an observation, and E the proposition that H would explain O if H and O were true. Then our claim is that Pr = Pr. We defended this screening-off thesis by discussing an example concerning smoking and cancer. Climenhaga argues that SOT is mistaken because it delivers the wrong verdict about a slightly different smoking-and-cancer case. He also considers a variant of SOT, called (...)
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  35. Belief Revision for Growing Awareness.Katie Steele & H. Orri Stefánsson - forthcoming - Mind.
    The Bayesian maxim for rational learning could be described as conservative change from one probabilistic belief or credence function to another in response to newinformation. Roughly: ‘Hold fixed any credences that are not directly affected by the learning experience.’ This is precisely articulated for the case when we learn that some proposition that we had previously entertained is indeed true (the rule of conditionalisation). But can this conservative-change maxim be extended to revising one’s credences in response to entertaining propositions or (...)
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  36.  75
    Resolving the Raven Paradox: Simple Random Sampling, Stratified Random Sampling, and Inference to the Best Explanation.Barry Ward - manuscript
    We object to standard, simple random sampling resolutions of the raven paradox on the grounds that they relevantly diverge from scientific practice. In response, we develop a stratified random sampling model. It provides a better fit and apparently rehabilitates simple random sampling resolutions as legitimate idealizations of that practice. However, neither simple nor stratified models fare well with a second concern, the objection from potential bias. In response, we develop a third model on which we systematically check kinds of ways (...)
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  37. Robustness Analysis as Explanatory Reasoning.Jonah N. Schupbach - 2016 - British Journal for the Philosophy of Science:axw008.
    When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be robust. This paper investigates the logic of such "robustness analysis" [RA]. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...)
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  38. In Defence of Dogmatism.Luca Moretti - 2015 - Philosophical Studies 172 (1):261-282.
    According to Jim Pryor’s dogmatism, when you have an experience with content p, you often have prima facie justification for believing p that doesn’t rest on your independent justification for believing any proposition. Although dogmatism has an intuitive appeal and seems to have an antisceptical bite, it has been targeted by various objections. This paper principally aims to answer the objections by Roger White according to which dogmatism is inconsistent with the Bayesian account of how evidence affects our rational credences. (...)
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  39. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - forthcoming - Philosophy and Technology:1-21.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...)
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  40. "The Bayesian Objection" - Ch 4 of Seemings and Epistemic Justification.Luca Moretti - 2020 - In Seemings and Epistemic Justification.
    In this chapter I analyse an objection to phenomenal conservatism to the effect that phenomenal conservatism is unacceptable because it is incompatible with Bayesianism. I consider a few responses to it and dismiss them as misled or problematic. Then, I argue that this objection doesn’t go through because it rests on an implausible formalization of the notion of seeming-based justification. In the final part of the chapter, I investigate how seeming-based justification and justification based on one’s reflective belief that (...)
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  41. 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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  42. Robustness, Diversity of Evidence, and Probabilistic Independence.Jonah N. Schupbach - 2015 - In Mäki, Ruphy, Schurz & Votsis (eds.), Recent Developments in the Philosophy of Science: EPSA13 Helsinki. Springer. pp. 305-316.
    In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...)
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  43. Begging the Question and Bayesians.Brian Weatherson - 1999 - Studies in History and Philosophy of Science Part A 30:687-697.
    The arguments for Bayesianism in the literature fall into three broad categories. There are Dutch Book arguments, both of the traditional pragmatic variety and the modern ‘depragmatised’ form. And there are arguments from the so-called ‘representation theorems’. The arguments have many similarities, for example they have a common conclusion, and they all derive epistemic constraints from considerations about coherent preferences, but they have enough differences to produce hostilities between their proponents. In a recent paper, Maher (1997) has argued that (...)
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  44. The Structure of Epistemic Probabilities.Nevin Climenhaga - 2020 - Philosophical Studies 177 (11):3213-3242.
    The epistemic probability of A given B is the degree to which B evidentially supports A, or makes A plausible. This paper is a first step in answering the question of what determines the values of epistemic probabilities. I break this question into two parts: the structural question and the substantive question. Just as an object’s weight is determined by its mass and gravitational acceleration, some probabilities are determined by other, more basic ones. The structural question asks what probabilities are (...)
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  45. Naturalness as a Constraint on Priors.Darren Bradley - 2020 - Mind 129 (513):179-203.
    Many epistemological problems can be solved by the objective Bayesian view that there are rationality constraints on priors, that is, inductive probabilities. But attempts to work out these constraints have run into such serious problems that many have rejected objective Bayesianism altogether. I argue that the epistemologist should borrow the metaphysician’s concept of naturalness and assign higher priors to more natural hypotheses.
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  46. Skepticism and Epistemic Closure: Two Bayesian Accounts.Luca Moretti & Tomoji Shogenji - 2017 - International Journal for the Study of Skepticism 7 (1):1-25.
    This paper considers two novel Bayesian responses to a well-known skeptical paradox. The paradox consists of three intuitions: first, given appropriate sense experience, we have justification for accepting the relevant proposition about the external world; second, we have justification for expanding the body of accepted propositions through known entailment; third, we do not have justification for accepting that we are not disembodied souls in an immaterial world deceived by an evil demon. The first response we consider rejects the third intuition (...)
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  47. 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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  48. Logical Ignorance and Logical Learning.Richard G. Pettigrew - forthcoming - Synthese:1-30.
    According to certain normative theories in epistemology, rationality requires us to be logically omniscient. Yet this prescription clashes with our ordinary judgments of rationality. How should we resolve this tension? In this paper, I focus particularly on the logical omniscience requirement in Bayesian epistemology. Building on a key insight by Ian Hacking (1967), I develop a version of Bayesianism that permits logical ignorance. This includes an account of the synchronic norms that govern a logically ignorant individual at any given (...)
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  49. Curve-Fitting for Bayesians?Gordon Belot - 2017 - British Journal for the Philosophy of Science 68 (3):689-702.
    Bayesians often assume, suppose, or conjecture that for any reasonable explication of the notion of simplicity a prior can be designed that will enforce a preference for hypotheses simpler in just that sense. But it is shown here that there are simplicity-driven approaches to curve-fitting problems that cannot be captured within the orthodox Bayesian framework.
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  50. Time-Slice Rationality.Brian Hedden - 2015 - Mind 124 (494):449-491.
    I advocate Time-Slice Rationality, the thesis that the relationship between two time-slices of the same person is not importantly different, for purposes of rational evaluation, from the relationship between time-slices of distinct persons. The locus of rationality, so to speak, is the time-slice rather than the temporally extended agent. This claim is motivated by consideration of puzzle cases for personal identity over time and by a very moderate form of internalism about rationality. Time-Slice Rationality conflicts with two proposed principles of (...)
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