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  1. New Boundary Lines.Alejandro Pérez Carballo - manuscript
    Intellectual progress involves forming a more accurate picture of the world. But it also figuring out which concepts to use for theorizing about the world. Bayesian epistemology has had much to say about the former aspect of our cognitive lives, but little if at all about the latter. I outline a framework for formulating questions about conceptual change in a broadly Bayesian framework. By enriching the resources of Epistemic Utility Theory with a more expansive conception of epistemic value, I offer (...)
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  2. Plausible Permissivism.Michael G. Titelbaum & Matthew Kopec - manuscript
    Abstract. Richard Feldman’s Uniqueness Thesis holds that “a body of evidence justifies at most one proposition out of a competing set of proposi- tions”. The opposing position, permissivism, allows distinct rational agents to adopt differing attitudes towards a proposition given the same body of evidence. We assess various motivations that have been offered for Uniqueness, including: concerns about achieving consensus, a strong form of evidentialism, worries about epistemically arbitrary influences on belief, a focus on truth-conduciveness, and consequences for peer disagreement. (...)
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  3. Divine Hiddenness and Other Evidence.Charity Anderson & Jeffrey Sanford Russell - forthcoming - In Oxford Studies in Philosophy of Religion.
    Many people do not know or believe there is a God, and many experience a sense of divine absence. Are these (and other) “divine hiddenness” facts evidence against the existence of God? Using Bayesian tools, we investigate *evidential arguments from divine hiddenness*, and respond to two objections to such arguments. The first objection says that the problem of hiddenness is just a special case of the problem of evil, and so if one has responded to the problem of evil then (...)
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  4. Why Boltzmann Brains Are Bad.Sean M. Carroll - forthcoming - In Shamik Dasgupta & Brad Weslake (eds.), Current Controversies in Philosophy of Science. Routledge.
    Some modern cosmological models predict the appearance of Boltzmann Brains: observers who randomly fluctuate out of a thermal bath rather than naturally evolving from a low-entropy Big Bang. A theory in which most observers are of the Boltzmann Brain type is generally thought to be unacceptable, although opinions differ. I argue that such theories are indeed unacceptable: the real problem is with fluctuations into observers who are locally identical to ordinary observers, and their existence cannot be swept under the rug (...)
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  5. An Epistemic Advantage of Accommodation Over Prediction.Finnur Dellsén - forthcoming - Philosophers' Imprint.
    Many philosophers have argued that a hypothesis is better confirmed by some data if the hypothesis was not specifically designed to fit the data. ‘Prediction’, they argue, is superior to ‘accommodation’. Others deny that there is any epistemic advantage to prediction, and conclude that prediction and accommodation are epistemically on a par. This paper argues that there is a respect in which accommodation is superior to prediction. Specifically, the information that the data was accommodated rather than predicted suggests that the (...)
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  6. 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 only static (...)
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  7. Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity.Patrick Grim, Frank Seidl, Calum McNamara, Hinton Rago, Isabell Astor, Caroline Diaso & Peter Ryner - forthcoming - Philosophy of Science.
    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 envisage and analyze the (...)
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  8. Multiple Universes and Self-Locating Evidence.Yoaav Isaacs, John Hawthorne & Jeffrey Sanford Russell - forthcoming - Philosophical Review.
    Is the fact that our universe contains fine-tuned life evidence that we live in a multiverse? Hacking (1987) and White (2000) influentially argue that it is not. We approach this question through a systematic framework for self-locating epistemology. As it turns out, leading approaches to self-locating evidence agree that the fact that our own universe contains fine-tuned life indeed confirms the existence of a multiverse (at least in a suitably idealized setting). This convergence is no accident: we present two theorems (...)
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  9. Is an Increase in Probability Always an Increase in Evidential Support?Artūrs Logins - forthcoming - Erkenntnis:1-25.
    Peter Achinstein has argued at length and on many occasions that the view according to which evidential support is defined in terms of probability-raising faces serious counterexamples and, hence, should be abandoned. Proponents of the positive probabilistic relevance view have remained unconvinced. The debate seems to be in a deadlock. This paper is an attempt to move the debate forward and revisit some of the central claims within this debate. My conclusion here will be that while Achinstein may be right (...)
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  10. Can All-Accuracy Accounts Justify Evidential Norms?Christopher J. G. Meacham - forthcoming - In Kristoffer Ahlstrom-Vij & Jeff Dunn (eds.), Epistemic Consequentialism. Oxford University Press.
    Some of the most interesting recent work in formal epistemology has focused on developing accuracy-based approaches to justifying Bayesian norms. These approaches are interesting not only because they offer new ways to justify these norms, but because they potentially offer a way to justify all of these norms by appeal to a single, attractive epistemic goal: having accurate beliefs. Recently, Easwaran & Fitelson (2012) have raised worries regarding whether such “all-accuracy” or “purely alethic” approaches can accommodate and justify evidential Bayesian (...)
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  11. Bayesian Beauty.Silvia Milano - forthcoming - Erkenntnis:1-20.
    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, vindicating why (...)
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  12. Entitlement, Epistemic Risk and Scepticism.Luca Moretti - forthcoming - Episteme:1-11.
    Crispin Wright maintains that the architecture of perceptual justification is such that we can acquire justification for our perceptual beliefs only if we have antecedent justification for ruling out any sceptical alternative. Wright contends that this principle doesn’t elicit scepticism, for we are non-evidentially entitled to accept the negation of any sceptical alternative. Sebastiano Moruzzi has challenged Wright’s contention by arguing that since our non-evidential entitlements don’t remove the epistemic risk of our perceptual beliefs, they don’t actually enable us to (...)
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  13. Epistemic Entitlement, Leaching and Epistemic Risk.Luca Moretti & Crispin Wright - forthcoming - Philosophical Topics.
    According to Crispin Wright, we have evidential justification for, or knowledge of, various propositions that we quotidianly accept only if we have antecedent justification for accepting general hinge propositions––called ‘cornerstones’––which cannot be evidentially supported. Wright contends that this doesn’t engender scepticism, for we are non-evidentially entitled to accept cornerstones. This paper focuses on the Leaching Worry––the concern that since the epistemic risk of accepting a cornerstone C without evidence for it is significantly high, the epistemic risk of accepting a proposition (...)
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  14. 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 time, (...)
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  15. Explanatory Coherence and the Impossibility of Confirmation by Coherence.Ted Poston - forthcoming - Philosophy of Science 88 (5).
    The coherence of independent reports provides a strong reason to believe that the reports are true. This plausible claim has come under attack from recent results in Bayesian epistemology. Huemer (1997), Olsson (2002, 2005), and Bovens and Hartmann (2003) prove that, under certain probabilistic conditions, coherence cannot increase the probability of the target claim. These results are taken to demonstrate that epistemic coherentism is untenable. To date no one has investigated how these Bayesian results bear on different conceptions of coherence. (...)
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  16. Which Models of Scientific Explanation Are (In)Compatible with IBE?Yunus Prasetya - forthcoming - British Journal for the Philosophy of Science.
    In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Philip Kitcher’s unificationist account supports IBE; Peter Railton’s deductive-nomological-probabilistic model, Wesley Salmon’s statistical-relevance Model, and Bas van (...)
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  17. 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 within (...)
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  18. 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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  19. Conglomerability, Disintegrability, and the Comparative Principle.Rush T. Stewart & Michael Nielsen - forthcoming - Analysis.
    Our aim here is to present a result that connects some approaches to justifying countable additivity. This result allows us to better understand the force of a recent argument for countable additivity due to Easwaran (2013). We have two main points. First, Easwaran’s argument in favor of countable additivity should have little persuasive force on those permissive probabilists who have already made their peace with violations of conglomerability. As our result shows, Easwaran’s main premise—the comparative principle—is strictly stronger than conglomerability. (...)
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  20. The epistemic impact of theorizing: generation bias implies evaluation bias.Finnur Dellsén - 2020 - Philosophical Studies 177 (12):3661-3678.
    It is often argued that while biases routinely influence the generation of scientific theories, a subsequent rational evaluation of such theories will ensure that biases do not affect which theories are ultimately accepted. Against this line of thought, this paper shows that the existence of certain kinds of biases at the generation-stage implies the existence of biases at the evaluation-stage. The key argumentative move is to recognize that a scientist who comes up with a new theory about some phenomena has (...)
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  21. 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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  22. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2020 - Philosophy of Science 87 (1):152-178.
    As stochastic independence is essential to the mathematical development of probability theory, it seems that any foundational work on probability should be able to account for this property. Bayesian decision theory appears to be wanting in this respect. Savage’s postulates on preferences under uncertainty entail a subjective expected utility representation, and this asserts only the existence and uniqueness of a subjective probability measure, regardless of its properties. What is missing is a preference condition corresponding to stochastic independence. To fill this (...)
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  23. "The Bayesian Objection" - Ch 4 of Seemings and Epistemic Justification.Luca Moretti - 2020 - In Seemings and Epistemic Justification. Springer.
    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 one (...)
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  24. On the Accuracy of Group Credences.Richard Pettigrew - 2020 - Oxford Studies in Epistemology 6.
    to appear in Szabó Gendler, T. & J. Hawthorne (eds.) Oxford Studies in Epistemology volume 6 We often ask for the opinion of a group of individuals. How strongly does the scientific community believe that the rate at which sea levels are rising increased over the last 200 years? How likely does the UK Treasury think it is that there will be a recession if the country leaves the European Union? What are these group credences that such questions request? And (...)
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  25. 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 longer depend (...)
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  26. An Automatic Ockham’s Razor for Bayesians?Gordon Belot - 2019 - Erkenntnis 84 (6):1361-1367.
    It is sometimes claimed that the Bayesian framework automatically implements Ockham’s razor—that conditionalizing on data consistent with both a simple theory and a complex theory more or less inevitably favours the simpler theory. It is shown here that the automatic razor doesn’t in fact cut it for certain mundane curve-fitting problems.
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  27. 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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  28. Learning and Value Change.J. Dmitri Gallow - 2019 - Philosophers' Imprint 19:1--22.
    Accuracy-first accounts of rational learning attempt to vindicate the intuitive idea that, while rationally-formed belief need not be true, it is nevertheless likely to be true. To this end, they attempt to show that the Bayesian's rational learning norms are a consequence of the rational pursuit of accuracy. Existing accounts fall short of this goal, for they presuppose evidential norms which are not and cannot be vindicated in terms of the single-minded pursuit of accuracy. I propose an alternative account, according (...)
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  29. Basing for the Bayesian.Cameron Gibbs - 2019 - Synthese 196 (9):3815-3840.
    There is a distinction between merely having the right belief, and further basing that belief on the right reasons. Any adequate epistemology needs to be able to accommodate the basing relation that marks this distinction. However, trouble arises for Bayesianism. I argue that when we combine Bayesianism with the standard approaches to the basing relation, we get the result that no agent forms their credences in the right way; indeed, no agent even gets close. This is a serious problem, for (...)
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  30. A New Probabilistic Explanation of the Modus Ponens–Modus Tollens Asymmetry.Stephan Hartmann, Benjamin Eva & Henrik Singmann - 2019 - In CogSci 2019 Proceedings. Montreal, Québec, Kanada: pp. 289–294.
    A consistent finding in research on conditional reasoning is that individuals are more likely to endorse the valid modus ponens (MP) inference than the equally valid modus tollens (MT) inference. This pattern holds for both abstract task and probabilistic task. The existing explanation for this phenomenon within a Bayesian framework (e.g., Oaksford & Chater, 2008) accounts for this asymmetry by assuming separate probability distributions for both MP and MT. We propose a novel explanation within a computational-level Bayesian account of reasoning (...)
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  31. Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G theory consists (...)
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  32. 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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  33. A Metacognitive Approach to Trust and a Case Study: Artificial Agency.Ioan Muntean - 2019 - Computer Ethics - Philosophical Enquiry (CEPE) Proceedings.
    Trust is defined as a belief of a human H (‘the trustor’) about the ability of an agent A (the ‘trustee’) to perform future action(s). We adopt here dispositionalism and internalism about trust: H trusts A iff A has some internal dispositions as competences. The dispositional competences of A are high-level metacognitive requirements, in the line of a naturalized virtue epistemology. (Sosa, Carter) We advance a Bayesian model of two (i) confidence in the decision and (ii) model uncertainty. To trust (...)
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  34. Chancy Modus Ponens.Sven Neth - 2019 - Analysis 79 (4):632-638.
    Chancy modus ponens is the following inference scheme: ‘probably φ’, ‘if φ, then ψ’, therefore, ‘probably ψ’. I argue that Chancy modus ponens is invalid in general. I further argue that the invalidity of Chancy modus ponens sheds new light on the alleged counterexample to modus ponens presented by McGee. I close by observing that, although Chancy modus ponens is invalid in general, we can recover a restricted sense in which this scheme of inference is valid.
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  35. Obligation, Permission, and Bayesian Orgulity.Michael Nielsen & Rush T. Stewart - 2019 - Ergo: An Open Access Journal of Philosophy 6.
    This essay has two aims. The first is to correct an increasingly popular way of misunderstanding Belot's Orgulity Argument. The Orgulity Argument charges Bayesianism with defect as a normative epistemology. For concreteness, our argument focuses on Cisewski et al.'s recent rejoinder to Belot. The conditions that underwrite their version of the argument are too strong and Belot does not endorse them on our reading. A more compelling version of the Orgulity Argument than Cisewski et al. present is available, however---a point (...)
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  36. Fading Foundations: Probability and the Regress Problem.William Roche - 2019 - Philosophical Quarterly 69 (274):212-215.
    Fading Foundations: Probability and the Regress Problem. By Atkinson David, Peijnenburg Jeanne.
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  37. Discrimination-Conduciveness and Observation Selection Effects.William Roche & Elliott Sober - 2019 - Philosophers' Imprint 19:1-26.
    We conceptualize observation selection effects (OSEs) by considering how a shift from one process of observation to another affects discrimination-conduciveness, by which we mean the degree to which possible observations discriminate between hypotheses, given the observation process at work. OSEs in this sense come in degrees and are causal, where the cause is the shift in process, and the effect is a change in degree of discrimination-conduciveness. We contrast our understanding of OSEs with others that have appeared in the literature. (...)
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  38. Permissivism and the Value of Rationality: A Challenge to the Uniqueness Thesis.Miriam Schoenfield - 2019 - Philosophy and Phenomenological Research 99 (2):286-297.
    In recent years, permissivism—the claim that a body of evidence can rationalize more than one response—has enjoyed somewhat of a revival. But it is once again being threatened, this time by a host of new and interesting arguments that, at their core, are challenging the permissivist to explain why rationality matters. A version of the challenge that I am especially interested in is this: if permissivism is true, why should we expect the rational credences to be more accurate than the (...)
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  39. Norm Conflicts and Conditionals.Niels Skovgaard-Olsen, David Kellen, Ulrike Hahn & Karl Christoph Klauer - 2019 - Psychological Review (5):611-633.
    Suppose that two competing norms, N1 and N2, can be identified such that a given person’s response can be interpreted as correct according to N1 but incorrect according to N2. Which of these two norms, if any, should one use to interpret such a response? In this paper we seek to address this fundamental problem by studying individual variation in the interpretation of conditionals by establishing individual profiles of the participants based on their case judgments and reflective attitudes. To investigate (...)
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  40. The Problem of Evaluating Automated Large-Scale Evidence Aggregators.Nicolas Wüthrich & Katie Steele - 2019 - Synthese (8):3083-3102.
    In the biomedical context, policy makers face a large amount of potentially discordant evidence from different sources. This prompts the question of how this evidence should be aggregated in the interests of best-informed policy recommendations. The starting point of our discussion is Hunter and Williams’ recent work on an automated aggregation method for medical evidence. Our negative claim is that it is far from clear what the relevant criteria for evaluating an evidence aggregator of this sort are. What is the (...)
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  41. 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 working hypothesis (...)
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  42. 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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  43. Solving the Problem of Logical Omniscience.Sinan Dogramaci - 2018 - Philosophical Issues 28 (1):107-128.
    This paper looks at three ways of addressing probabilism’s implausible requirement of logical omniscience. The first and most common strategy says it’s okay to require an ideally rational person to be logically omniscient. I argue that this view is indefensible on any interpretation of ‘ideally rational’. The second strategy says probabilism should be formulated not in terms of logically possible worlds but in terms of doxastically possible worlds, ways you think the world might be. I argue that, on the interpretation (...)
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  44. A Probabilistic Analysis of Argument Cogency.David Godden & Frank Zenker - 2018 - Synthese 195 (4):1715-1740.
    This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and (...)
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  45. 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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  46. 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 in (...)
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  47. Rational Analysis, Intractability, and the Prospects of ‘as If’-Explanations.Iris van Rooij, Cory Wright, Johan Kwisthout & Todd Wareham - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  48. R Codes for Preliminary Checks (Bayesian Method).Quan-Hoang Vuong - 2018 - SSHPA-AISDL 2018 (1):1-5.
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  49. Is Evidence of Evidence Evidence? Screening-Off Vs. No-Defeaters.Roche William - 2018 - Episteme 15 (4):451-462.
    I argue elsewhere (Roche 2014) that evidence of evidence is evidence under screening-off. Tal and Comesaña (2017) argue that my appeal to screening-off is subject to two objections. They then propose an evidence of evidence thesis involving the notion of a defeater. There is much to learn from their very careful discussion. I argue, though, that their objections fail and that their evidence of evidence thesis is open to counterexample.
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  50. 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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