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Uncertain Inference

Cambridge University Press (2001)

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  1. Defeasible Conditionalization.Paul D. Thorn - 2014 - Journal of Philosophical Logic 43 (2-3):283-302.
    The applicability of Bayesian conditionalization in setting one’s posterior probability for a proposition, α, is limited to cases where the value of a corresponding prior probability, PPRI(α|∧E), is available, where ∧E represents one’s complete body of evidence. In order to extend probability updating to cases where the prior probabilities needed for Bayesian conditionalization are unavailable, I introduce an inference schema, defeasible conditionalization, which allows one to update one’s personal probability in a proposition by conditioning on a proposition that represents a (...)
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  • Two Problems of Direct Inference.Paul D. Thorn - 2012 - Erkenntnis 76 (3):299-318.
    The article begins by describing two longstanding problems associated with direct inference. One problem concerns the role of uninformative frequency statements in inferring probabilities by direct inference. A second problem concerns the role of frequency statements with gerrymandered reference classes. I show that past approaches to the problem associated with uninformative frequency statements yield the wrong conclusions in some cases. I propose a modification of Kyburg’s approach to the problem that yields the right conclusions. Past theories of direct inference have (...)
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  • Direct Inference From Imprecise Frequencies.Paul D. Thorn - 2017 - In Michela Massimi, Jan-Willem Romeijn & Gerhard Schurz (eds.), EPSA15 Selected Papers - The 5th conference of the European Philosophy of Science. Springer. pp. 347-358.
    It is well known that there are, at least, two sorts of cases where one should not prefer a direct inference based on a narrower reference class, in particular: cases where the narrower reference class is gerrymandered, and cases where one lacks an evidential basis for forming a precise-valued frequency judgment for the narrower reference class. I here propose (1) that the preceding exceptions exhaust the circumstances where one should not prefer direct inference based on a narrower reference class, and (...)
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  • Undercutting Defeat Via Reference Properties of Differing Arity: A Reply to Pust.Paul D. Thorn - 2011 - Analysis 71 (4):662-667.
    In a recent article, Joel Pust argued that direct inference based on reference properties of differing arity are incommensurable, and so direct inference cannot be used to resolve the Sleeping Beauty problem. After discussing the defects of Pust's argument, I offer reasons for thinking that direct inferences based on reference properties of differing arity are commensurable, and that we should prefer direct inferences based on logically stronger reference properties, regardless of arity.
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  • Grounding Reichenbach’s Pragmatic Vindication of Induction.Michael J. Shaffer - 2017 - Polish Journal of Philosophy 11 (1):43-55.
    This paper has three interdependent aims. The first is to make Reichenbach’s views on induction and probabilities clearer, especially as they pertain to his pragmatic justification of induction. The second aim is to show how his view of pragmatic justification arises out of his commitment to extensional empiricism and moots the possibility of a non-pragmatic justification of induction. Finally, and most importantly, a formal decision-theoretic account of Reichenbach’s pragmatic justification is offered in terms both of the minimax principle and the (...)
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  • Demystifying Dilation.Arthur Paul Pedersen & Gregory Wheeler - 2014 - Erkenntnis 79 (6):1305-1342.
    Dilation occurs when an interval probability estimate of some event E is properly included in the interval probability estimate of E conditional on every event F of some partition, which means that one’s initial estimate of E becomes less precise no matter how an experiment turns out. Critics maintain that dilation is a pathological feature of imprecise probability models, while others have thought the problem is with Bayesian updating. However, two points are often overlooked: (1) knowing that E is stochastically (...)
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  • Direct Inference in the Material Theory of Induction.William Peden - unknown
    John D. Norton’s “Material Theory of Induction” has been one of the most intriguing recent additions to the philosophy of induction. Norton’s account appears to be a notably natural account of actual inductive practices, though his theory has attracted considerable criticisms. I detail several novel issues for his theory, but argue that supplementing the Material Theory with a theory of direct inference could address these problems. I argue that if this combination is possible, a stronger theory of inductive reasoning emerges, (...)
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  • Objective Bayesian Calibration and the Problem of Non-Convex Evidence.Gregory Wheeler - 2012 - British Journal for the Philosophy of Science 63 (4):841-850.
    Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence. One role of the calibration norm is to ensure that evidence works to constrain a convex set of probability functions. This essay brings into focus a problem for Williamson's theory when qualitative evidence specifies non-convex constraints.
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  • Uncertainty, Learning, and the “Problem” of Dilation.Seamus Bradley & Katie Siobhan Steele - 2013 - Erkenntnis 79 (6):1287-1303.
    Imprecise probabilism—which holds that rational belief/credence is permissibly represented by a set of probability functions—apparently suffers from a problem known as dilation. We explore whether this problem can be avoided or mitigated by one of the following strategies: (a) modifying the rule by which the credal state is updated, (b) restricting the domain of reasonable credal states to those that preclude dilation.
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  • Towards a Model of Argument Strength for Bipolar Argumentation Graphs.Erich Rast - 2018 - Studies in Logic, Grammar and Rhetoric 55 (1):31-62.
    Bipolar argument graphs represent the structure of complex pro and contra arguments for one or more standpoints. In this article, ampliative and exclusionary principles of evaluating argument strength in bipolar acyclic argumentation graphs are laid out and compared to each other. Argument chains, linked arguments, link attackers and supporters, and convergent arguments are discussed. The strength of conductive arguments is also addressed but it is argued that more work on this type of argument is needed to properly distinguish argument strength (...)
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  • Why Frequentists and Bayesians Need Each Other.Jon Williamson - 2013 - Erkenntnis 78 (2):293-318.
    The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.
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  • Epistemology Formalized.Sarah Moss - 2013 - Philosophical Review 122 (1):1-43.
    This paper argues that just as full beliefs can constitute knowledge, so can properties of your credence distribution. The resulting notion of probabilistic knowledge helps us give a natural account of knowledge ascriptions embedding language of subjective uncertainty, and a simple diagnosis of probabilistic analogs of Gettier cases. Just like propositional knowledge, probabilistic knowledge is factive, safe, and sensitive. And it helps us build knowledge-based norms of action without accepting implausible semantic assumptions or endorsing the claim that knowledge is interest-relative.
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  • Modelling Phenomena and Dynamic Logic of Phenomena.Boris Kovalerchuk, Leonid Perlovsky & Gregory Wheeler - 2012 - Journal of Applied Non-Classical Logics 22 (1-2):53-82.
    Modelling a complex phenomenon such as the mind presents tremendous computational complexity challenges. Modelling field theory addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena for model (...)
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  • Modeling of Phenomena and Dynamic Logic of Phenomena.Boris Kovalerchuk, Leonid Perlovsky & Gregory Wheeler - 2011 - Journal of Applied Non-Classical Logic 22 (1):1-82.
    Modeling a complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, a problem or some theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called (...)
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  • Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.Vincenzo Crupi, Jonathan D. Nelson, Björn Meder, Gustavo Cevolani & Katya Tentori - 2018 - Cognitive Science 42 (5):1410-1456.
    Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people’s goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the (...)
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  • Subjunctive Credences and Semantic Humility.Sarah Moss - 2013 - Philosophy and Phenomenological Research 87 (2):251-278.
    This paper argues that several leading theories of subjunctive conditionals are incompatible with ordinary intuitions about what credences we ought to have in subjunctive conditionals. In short, our theory of subjunctives should intuitively display semantic humility, i.e. our semantic theory should deliver the truth conditions of sentences without pronouncing on whether those conditions actually obtain. In addition to describing intuitions about subjunctive conditionals, I argue that we can derive these ordinary intuitions from justified premises, and I answer a possible worry (...)
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  • Reasoning From Conflicting Sources.Gilbert Plumer & Kenneth Olson - 2007 - In Hans V. Hansen, Christopher W. Tindale, J. Anthony Blair, Ralph H. Johnson & David M. Godden (eds.), Dissensus and the Search for Common Ground. Proceedings 2007 [CD-ROM]. Ontario Society for the Study of Argumentation. pp. 1-9.
    One might ask of two or more texts—what can be inferred from them, taken together? If the texts happen to contradict each other in some respect, then the unadorned answer of standard logic is EVERYTHING. But it seems to be a given that we often successfully reason with inconsistent information from multiple sources. The purpose of this paper is to attempt to develop an adequate approach to accounting for this given.
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  • Machine Epistemology and Big Data.Gregory Wheeler - 2016 - In Lee McIntyre & Alex Rosenburg (eds.), Routledge Companion to Philosophy of Social Science. Routledge.
    In the age of big data and a machine epistemology that can anticipate, predict, and intervene on events in our lives, the problem once again is that a few individuals possess the knowledge of how to regulate these activities. But the question we face now is not how to share such knowledge more widely, but rather of how to enjoy the public benefits bestowed by this knowledge without freely sharing it. It is not merely personal privacy that is at stake (...)
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  • Internalism and the Generality Problem.Masashi Kasaki - 2012 - Kagaku Tetsugaku 45 (2):83-98.
    The generality problem is usually taken to arise only for externalist theories of knowledge or justification. In this paper, first, I argue that even internalist theories run afoul of a variant of the generality problem. This is because S may have multiple pieces of evidence concerning the reliability of the token process by which S forms the belief in question, and they determine the degree of Sʼs internalist justification differently. Second, I offer a solution to the generality problem for internalist (...)
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