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1907 found
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1 — 50 / 1907
  1. Knowledge in Action.Jonathan Weisberg - 2013 - Philosophers' Imprint 13.
    Recent proposals that frame norms of action in terms of knowledge have been challenged by Bayesian decision theorists. Bayesians object that knowledge-based norms conflict with the highly successful and established view that rational action is rooted in degrees of belief. I argue that the knowledge-based and Bayesian pictures are not as incompatible as these objectors have made out. Attending to the mechanisms of practical reasoning exposes space for both knowledge and degrees of belief to play their respective roles.
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  2. 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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  3. An Even Better Solution to the Paradox of the Ravens.James Hawthorne & Branden Fitelson - manuscript
    Think of confirmation in the context of the Ravens Paradox this way. The likelihood ratio measure of incremental confirmation gives us, for an observed Black Raven and for an observed non-Black non-Raven, respectively, the following “full” likelihood ratios.
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  4. How Should Your Beliefs Change When Your Awareness Grows?Richard Pettigrew - manuscript
    Epistemologists who study partial beliefs, or credences, have a well-developed account of how you should change your credences when you learn new evidence; that is, when your body of evidence grows. What's more, they boast a diverse range of epistemic and pragmatic arguments that support that account. But they do not have a satisfactory account of when and how you should change your credences when you become aware of possibilities and propositions you have not entertained before; that is, when your (...)
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  5. Probability Without Tears.Julia Staffel - manuscript
    This paper is about teaching probability to graduate and undergraduate students of philosophy who don’t aim to do primarily formal work in their research. These students are unlikely to seek out classes that are explicitly about probability or formal epistemology for various reasons, for example because they don’t realize that this knowledge would be useful for them or because they are intimidated by the material. However, most areas of philosophy now contain debates that incorporate probability, and basic knowledge of it (...)
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  6. 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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  7. Can Knowledge Be Justified True Belief? (Pdf 69k).Ken Binmore - manuscript
    Knowledge was traditionally held to be justified true belief. This paper examines the implications of maintaining this view if justication is interpreted algorithmically. It is argued that if we move sufficiently far from the small worlds to which Bayesian decision theory properly applies, we can steer between the rock of fallibilism and the whirlpool of skepticism only by explicitly building into our framing of the underlying decision problem the possibility that its attempt to describe the world is inadequate.
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  8. Making Decisions in Large Worlds (Pdf 141k).Ken Binmore - manuscript
    This paper argues that we need to look beyond Bayesian decision theory for an answer to the general problem of making rational decisions under uncertainty. The view that Bayesian decision theory is only genuinely valid in a small world was asserted very firmly by Leonard Savage [18] when laying down the principles of the theory in his path-breaking Foundations of Statistics. He makes the distinction between small and large worlds in a folksy way by quoting the proverbs ”Look before you (...)
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  9. Inductive Rules Are No Problem.Daniel Steel - manuscript
    This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. According to this proposal, background knowledge is important in the application of inductive rules and Bayesianism qualifies as an inductive rule. Finally, I consider a (...)
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  10. Bayesian Epistemology.Robert Williams - manuscript
    Synthese 156 (3) (2007). Special issue ed. with Luc Bovens. With contributions by Max Albert, Branden Fitelson, Dennis Dieks, Igor Douven and Wouter Meijs, Alan Hájek, Colin Howson, James Joyce, and Patrick Suppes.
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  11. A Basic Course in Probability Theory.Rabi Bhattacharya & Edward C. Waymire - forthcoming - Analysis.
    The book develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. With this goal in mind, the pace is lively, yet thorough. Basic notions of independence and conditional expectation are introduced relatively early on in the text, while conditional expectation is illustrated in detail in the context of martingales, Markov property and strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two highlights. The historic role of size-biasing (...)
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  12. 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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  13. Causal Reasoning and Meno’s Paradox.Melvin Chen & Lock Yue Chew - forthcoming - AI and Society:1-9.
    Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has (...)
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  14. 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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  15. 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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  16. Degrees of Acceptance.Alexander Dinges - forthcoming - Philosophical Quarterly.
    While many authors distinguish belief from acceptance, it seems almost universally agreed that no similar distinction can be drawn between degrees of belief, or credences, and degrees of acceptance. I challenge this assumption in this paper. Acceptance comes in degrees and acknowledging this helps to resolve problems in at least two philosophical domains. Degrees of acceptance play vital roles when we simplify our reasoning, and they ground the common ground of a conversation if we assume context probabilism, i.e., that the (...)
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  17. Broadband or Bust!George F. Gilder & John Wohlstetter - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
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  18. 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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  19. Bayes Nets and Rationality.Stephan Hartmann - forthcoming - In The Handbook of Rationality. Boston, Massachusetts, USA:
    Bayes nets are a powerful tool for researchers in statistics and artificial intelligence. This chapter demonstrates that they are also of much use for philosophers and psychologists interested in (Bayesian) rationality. To do so, we outline the general methodology of Bayes nets modeling in rationality research and illustrate it with several examples from the philosophy and psychology of reasoning and argumentation. Along the way, we discuss the normative foundations of Bayes nets modeling and address some of the methodological problems it (...)
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  20. Reasonable Doubt and Alternative Hypotheses: A Bayesian Analysis.Stephan Hartmann & Ulrike Hahn - forthcoming - Journal.
    A longstanding question is the extent to which "reasonable doubt" may be expressed simply in terms of a threshold degree of belief. In this context, we examine the extent to which learning about possible alternatives may alter one's beliefs about a target hypothesis, even when no new "evidence" linking them to the hypothesis is acquired. Imagine the following scenario: a crime has been committed and Alice, the police's main suspect has been brought to trial. There are several pieces of evidence (...)
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  21. (Almost) All Evidence is Higher-Order Evidence.Brian Hedden & Kevin Dorst - forthcoming - Analysis.
    Higher-order evidence is evidence about what’s rational to think in light of your evidence. Many have argued that it’s special—falling into its own evidential category, or leading to deviations from standard rational norms. But it’s not. Given standard assumptions, almost all evidence is (in part) higher-order evidence.
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  22. Prediction Error Minimization, Mental and Developmental Disorder, and Statistical Theories of Consciousness.Jakob Hohwy - forthcoming - In Rocco Gennaro (ed.), Disturbed Consciousness: New Essays on Psychopathology and Theories of Consciousness. MIT Press.
    This chapter seeks to recover an approach to consciousness from a general theory of brain function, namely the prediction error minimization theory. The way this theory applies to mental and developmental disorder demonstrates its relevance to consciousness. The resulting view is discussed in relation to a contemporary theory of consciousness, namely the idea that conscious perception depends on Bayesian metacognition; this theory is also supported by considerations of psychopathology. This Bayesian theory is first disconnected from the higher-order thought theory, and (...)
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  23. Hydrography: Compiling and Updating the Nautical Chart.B. Hutton - forthcoming - Veritas – Revista de Filosofia da Pucrs.
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  24. 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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  25. A Probabilistic Analysis of Title IX Reforms.Yoaav Isaacs & Jason Iuliano - forthcoming - Journal of Political Philosophy.
    In 2011, the Office for Civil Rights made substantial changes to the regulations governing campus sexual assault investigations. These changes were the subject of significant controversy, and in 2017 the Department of Education issued further guidance, contravening some—but not all—of the 2011 reforms. In light of this action, regulations governing campus sexual assault investigations continue to be the focus of intense debate, and their future is far from certain. Despite this sharp disagreement between supporters and opponents of the reforms, a (...)
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  26. Immortal Beauty: Does Existence Confirm Reincarnation?Jens Jäger - forthcoming - Australasian Journal of Philosophy.
    I argue that a popular view about self-locating evidence implies that there are cases in which agents have surprisingly strong evidence for their own reincarnation. The central case is an ‘Immortal Beauty' scenario, modelled after the well-known Sleeping Beauty puzzle. I argue that if the popular ‘thirder’ solution to the puzzle is correct, then Immortal Beauty should be confident that she's going to be reincarnated. The essay also examines another pro-reincarnation argument due to Michael Huemer (2021). I argue that his (...)
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  27. 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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  28. 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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  29. 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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  30. Epistemic Entitlement, Leaching and Epistemic Risk.Luca Moretti & Crispin Wright - forthcoming - Philosophy and Phenomenological Research.
    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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  31. Kolmogorov Conditionalization, A New Argument For.Michael Nielsen - forthcoming - Review of Symbolic Logic:1-16.
    This paper contributes to a recent research program that extends arguments supporting elementary conditionalization to arguments supporting conditionalization with general, measure-theoretic conditional probabilities. I begin by suggesting an amendment to the framework that Rescorla (2018) has used to characterize regular conditional probabilities in terms of avoiding Dutch book. If we wish to model learning scenarios in which an agent gains complete membership knowledge about some subcollection of the events of interest to her, then we should focus on updating policies that (...)
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  32. That’s Not IBE: Reply to Park.Yunus Prasetya - forthcoming - Axiomathes:1-7.
    Park (2017, 2018, 2019) argues that Bas van Fraassen uses inference to the best explanation to defend his contextual theory of explanation. If Park is right, then van Fraassen is in trouble because he rejects IBE as a rational rule of inference. In this reply, I argue that van Fraassen does not use IBE in defending the contextual theory of explanation. I distinguish between several conceptions of IBE: heuristic IBE, objective Bayesian IBE, and ampliative IBE. I argue that van Fraassen (...)
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  33. 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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  34. 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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  35. Bayesian Norms and Non-Ideal Agents.Julia Staffel - forthcoming - In Maria Lasonen-Aarnio & Clayton M. Littlejohn (eds.), Routledge Handbook of the Philosophy Evidence. Routledge.
    Bayesian epistemology provides a popular and powerful framework for modeling rational norms on credences, including how rational agents should respond to evidence. The framework is built on the assumption that ideally rational agents have credences, or degrees of belief, that are representable by numbers that obey the axioms of probability. From there, further constraints are proposed regarding which credence assignments are rationally permissible, and how rational agents’ credences should change upon learning new evidence. While the details are hotly disputed, all (...)
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  36. Bayes, God, and the Multiverse.Richard Swinburne - forthcoming - Philosophical Explorations.
  37. Divine Hiddenness and Other Evidence.Charity Anderson & Jeffrey Sanford Russell - 2021 - 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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  38. Entitlement, Epistemic Risk and Scepticism.Luca Moretti - 2021 - Episteme 18 (4):576-586.
    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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  39. A note on deterministic updating and van Fraassen’s symmetry argument for conditionalization.Richard Pettigrew - 2021 - Philosophical Studies 178 (2):665-673.
    In a recent paper, Pettigrew argues that the pragmatic and epistemic arguments for Bayesian updating are based on an unwarranted assumption, which he calls deterministic updating, and which says that your updating plan should be deterministic. In that paper, Pettigrew did not consider whether the symmetry arguments due to Hughes and van Fraassen make the same assumption Scientific inquiry in philosophical perspective. University Press of America, Lanham, pp. 183–223, 1987). In this note, I show that they do.
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  40. 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 time; (...)
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  41. Interpreting Connexive Principles in Coherence-Based Probability Logic.Niki Pfeifer & Giuseppe Sanfilippo - 2021 - In J. Vejnarová & J. Wilson (eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021, LNAI 12897). Cham: pp. 672-687.
    We present probabilistic approaches to check the validity of selected connexive principles within the setting of coherence. Connexive logics emerged from the intuition that conditionals of the form If ∼A, then A, should not hold, since the conditional’s antecedent ∼A contradicts its consequent A. Our approach covers this intuition by observing that for an event A the only coherent probability assessment on the conditional event A|~A is p(A|~A)=0 . Moreover, connexive logics aim to capture the intuition that conditionals should express (...)
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  42. Explanatory Coherence and the Impossibility of Confirmation by Coherence.Ted Poston - 2021 - Philosophy of Science 88 (5):835-848.
    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 work in Bayesian epistemology. This work shows that, under certain probabilistic conditions, coherence cannot increase the probability of the target claim. These theorems are taken to demonstrate that epistemic coherentism is untenable. To date no one has investigated how these results bear on different conceptions of coherence. I investigate this situation using Thagard’s ECHO model of (...)
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  43. Belief Revision for Growing Awareness.Katie Steele & H. Orri Stefánsson - 2021 - Mind 130 (520):1207–1232.
    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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  44. Conglomerability, Disintegrability and the Comparative Principle.Rush T. Stewart & Michael Nielsen - 2021 - Analysis 81 (3):479-488.
    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. We have two main points. First, Easwaran’s argument in favour 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 premiss – the comparative principle – is strictly (...)
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  45. Bayesian Belief Revision Based on Agent’s Criteria.Yongfeng Yuan - 2021 - Studia Logica 109 (6):1311-1346.
    In the literature of belief revision, it is widely accepted that: there is only one revision phase in belief revision which is well characterized by the Bayes’ Rule, Jeffrey’s Rule, etc.. However, as I argue in this article, there are at least four successive phases in belief revision, namely first/second order evaluation and first/second order revision. To characterize these phases, I propose mainly four rules of belief revision based on agent’s criteria, and make one composition rule to characterize belief revision (...)
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  46. The Requirement of Total Evidence: A Reply to Epstein’s Critique.Martin Barrett & Elliott Sober - 2020 - Philosophy of Science 87 (1):191-203.
    The requirement of total evidence is a mainstay of Bayesian epistemology. Peter Fisher Epstein argues that the requirement generates mistaken conclusions about several examples that he devises. Here we examine the example of Epstein’s that we find most interesting and argue that Epstein’s analysis of it is flawed.
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  47. What Comes to Mind?Adam Bear, Samantha Bensinger, Julian Jara-Ettinger, Joshua Knobe & Fiery Cushman - 2020 - Cognition 194:104057.
    When solving problems, like making predictions or choices, people often “sample” possibilities into mind. Here, we consider whether there is structure to the kinds of thoughts people sample by default—that is, without an explicit goal. Across three experiments we found that what comes to mind by default are samples from a probability distribution that combines what people think is likely and what they think is good. Experiment 1 found that the first quantities that come to mind for everyday behaviors and (...)
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  48. Papias's Prologue and the Probability of Parallels.Nevin Climenhaga - 2020 - Journal of Biblical Literature 139 (3):591-596.
    Several scholars, including Martin Hengel, R. Alan Culpepper, and Richard Bauckham, have argued that Papias had knowledge of the Gospel of John on the grounds that Papias’s prologue lists six of Jesus’s disciples in the same order that they are named in the Gospel of John: Andrew, Peter, Philip, Thomas, James, and John. In “A Note on Papias’s Knowledge of the Fourth Gospel” (JBL 129 [2010]: 793–794), Jake H. O’Connell presents a statistical analysis of this argument, according to which the (...)
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  49. 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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  50. Confirmation Based on Analogical Inference: Bayes Meets Jeffrey.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2020 - Canadian Journal of Philosophy 50 (2):174-194.
    Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (in press) idea of analyzing confirmation based on analogical inference Baysian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a (...)
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