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  1. Decision Theory.Katie Steele & H. Orri Stefánsson - 2012 - In Peter Adamson (ed.), Stanford Encyclopedia of Philosophy. Stanford Encyclopedia of Philosophy.
  • Questioning to resolve decision problems.Robert van Rooy - 2003 - Linguistics and Philosophy 26 (6):727-763.
    Why do we ask questions? Because we want tohave some information. But why this particular kind ofinformation? Because only information of this particularkind is helpful to resolve the decision problemthat the agent faces. In this paper I argue thatquestions are asked because their answers help toresolve the questioner's decision problem, and that thisassumption helps us to interpret interrogativesentences. Interrogative sentences are claimed to have asemantically underspecified meaning and thisunderspecification is resolved by means of the decisionproblem.
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  • The structure of radical probabilism.Brian Skyrms - 1996 - Erkenntnis 45 (2-3):285 - 297.
    Does the philosophy of Radical Probabilism have enough structure to enable it to address fundamental epistemological questions? The requirement of dynamic coherence provides the structure for radical probabilist epistemology. This structure is sufficient to establish (i) the value of knowledge and (ii) long run convergence of degrees of belief.
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  • The Evolution of Bayesian Updating.Samir Okasha - 2013 - Philosophy of Science 80 (5):745-757.
    An evolutionary basis for Bayesian rationality is suggested, by considering how natural selection would operate on an organism’s ‘policy’ for choosing an action depending on an environmental signal. It is shown that the evolutionarily optimal policy, as judged by the criterion of maximal expected reproductive output, is the policy that, for each signal, picks an action that maximizes conditional expected output given that signal. This suggests a possible route by which Bayes-rational creatures might have evolved.
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  • Rational Aversion to Information.Sven Neth - forthcoming - British Journal for the Philosophy of Science.
    Is more information always better? Or are there some situations in which more information can make us worse off? Good (1967) argues that expected utility maximizers should always accept more information if the information is cost-free and relevant. But Good's argument presupposes that you are certain you will update by conditionalization. If we relax this assumption and allow agents to be uncertain about updating, these agents can be rationally required to reject free and relevant information. Since there are good reasons (...)
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  • Learning experiences and the value of knowledge.Simon M. Huttegger - 2014 - Philosophical Studies 171 (2):279-288.
    Generalized probabilistic learning takes place in a black-box where present probabilities lead to future probabilities by way of a hidden learning process. The idea that generalized learning can be partially characterized by saying that it doesn’t foreseeably lead to harmful decisions is explored. It is shown that a martingale principle follows for finite probability spaces.
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  • Generalized Learning and Conditional Expectation.Simon M. Huttegger & Michael Nielsen - 2020 - Philosophy of Science 87 (5):868-883.
    Reflection and martingale principles are central to models of rational learning. They can be justified in a variety of ways. In what follows we study martingale and reflection principles in the con...
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  • The Risk GP Model: The Standard Model of Prediction in Medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
  • The Risk GP Model: The standard model of prediction in medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
    With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target (...)
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  • The value of cost-free uncertain evidence.Patryk Dziurosz-Serafinowicz & Dominika Dziurosz-Serafinowicz - 2021 - Synthese 199 (5-6):13313-13343.
    We explore the question of whether cost-free uncertain evidence is worth waiting for in advance of making a decision. A classical result in Bayesian decision theory, known as the value of evidence theorem, says that, under certain conditions, when you update your credences by conditionalizing on some cost-free and certain evidence, the subjective expected utility of obtaining this evidence is never less than the subjective expected utility of not obtaining it. We extend this result to a type of update method, (...)
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  • The Value of Evidence and Ratificationism.Patryk Dziurosz-Serafinowicz - forthcoming - Erkenntnis:1-25.
    In sequential decision problems, an act of learning cost-free evidence might be symptomatic, in the sense that performing this act itself provides evidence about states of the world it does nothing to causally promote. It is well known that orthodox causal decision theory, like its main rival evidential decision theory, may sanction such acts as rationally impermissible. This paper shows that, under plausible assumptions, a minimal version of ratificationist causal decision theory, known as principled ratificationism, fares better in this respect, (...)
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  • Rational Polarization.Kevin Dorst - 2023 - Philosophical Review 132 (3):355-458.
    Predictable polarization is everywhere: we can often predict how people’s opinions, including our own, will shift over time. Extant theories either neglect the fact that we can predict our own polarization, or explain it through irrational mechanisms. They needn’t. Empirical studies suggest that polarization is predictable when evidence is ambiguous, that is, when the rational response is not obvious. I show how Bayesians should model such ambiguity and then prove that—assuming rational updates are those which obey the value of evidence—ambiguity (...)
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  • The Value of Biased Information.Nilanjan Das - 2023 - British Journal for the Philosophy of Science 74 (1):25-55.
    In this article, I cast doubt on an apparent truism, namely, that if evidence is available for gathering and use at a negligible cost, then it’s always instrumentally rational for us to gather that evidence and use it for making decisions. Call this ‘value of information’ (VOI). I show that VOI conflicts with two other plausible theses. The first is the view that an agent’s evidence can entail non-trivial propositions about the external world. The second is the view that epistemic (...)
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  • Credal imprecision and the value of evidence.Nilanjan Das - 2023 - Noûs 57 (3):684-721.
    This paper is about a tension between two theses. The first is Value of Evidence: roughly, the thesis that it is always rational for an agent to gather and use cost‐free evidence for making decisions. The second is Rationality of Imprecision: the thesis that an agent can be rationally required to adopt doxastic states that are imprecise, i.e., not representable by a single credence function. While others have noticed this tension, I offer a new diagnosis of it. I show that (...)
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  • Can free evidence be bad? Value of informationfor the imprecise probabilist.Seamus Bradley & Katie Steele - 2016 - Philosophy of Science 83 (1):1-28.
    This paper considers a puzzling conflict between two positions that are each compelling: it is irrational for an agent to pay to avoid `free' evidence before making a decision, and rational agents may have imprecise beliefs and/or desires. Indeed, we show that Good's theorem concerning the invariable choice-worthiness of free evidence does not generalise to the imprecise realm, given the plausible existing decision theories for handling imprecision. A key ingredient in the analysis, and a potential source of controversy, is the (...)
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  • Cambridge and Vienna: Frank P. Ramsey and the Vienna Circle.Maria Carla Galavotti (ed.) - 2004 - Dordrecht: Springer Verlag.
    The Institute Vienna Circle held a conference in Vienna in 2003, Cambridge and Vienna a?
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  • Normative uncertainty and information value.Riley Harris - 2021 - Dissertation, University of Adelaide
    This thesis is about making decisions when we are uncertain about what will happen, how valuable it will be, and even how to make decisions. Even the most sure-footed amongst us are sometimes uncertain about all three, but surprisingly little attention has been given to the latter two. The three essays that constitute my thesis hope to do a small part in rectifying this problem. The first essay is about the value of finding out how to make decisions. Society spends (...)
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  • Pluralists about Pluralism? Versions of Explanatory Pluralism in Psychiatry.Jeroen Van Bouwel - 2014 - In M. C. Galavotti, D. Dieks, W. J. Gonzalez, S. Hartmann, Th Uebel & M. Weber (eds.), New Directions in Philosophy of Science (The Philosophy of Science in a European Perspective Series). Springer. pp. 105-119.
    In this contribution, I comment on Raffaella Campaner’s defense of explanatory pluralism in psychiatry (in this volume). In her paper, Campaner focuses primarily on explanatory pluralism in contrast to explanatory reductionism. Furthermore, she distinguishes between pluralists who consider pluralism to be a temporary state on the one hand and pluralists who consider it to be a persisting state on the other hand. I suggest that it would be helpful to distinguish more than those two versions of pluralism – different understandings (...)
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  • Probability as a guide to life.Helen Beebee & David Papineau - 2003 - In David Papineau (ed.), The Roots of Reason. Oxford: Oxford University Press. pp. 217-243.
  • On argument strength.Niki Pfeifer - 2013 - In Frank Zenker (ed.), Bayesian argumentation. The practical side of probability. Dordrecht, Netherlands: pp. 185-193.
    Everyday life reasoning and argumentation is defeasible and uncertain. I present a probability logic framework to rationally reconstruct everyday life reasoning and argumentation. Coherence in the sense of de Finetti is used as the basic rationality norm. I discuss two basic classes of approaches to construct measures of argument strength. The first class imposes a probabilistic relation between the premises and the conclusion. The second class imposes a deductive relation. I argue for the second class, as the first class is (...)
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  • Dilation, Disintegrations, and Delayed Decisions.Arthur Paul Pedersen & Gregory Wheeler - 2015 - In Thomas Augistin, Serena Dora, Enrique Miranda & Erik Quaeghebeur (eds.), Proceedings of the 9th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2015). Aracne Editrice. pp. 227–236.
    Both dilation and non-conglomerability have been alleged to conflict with a fundamental principle of Bayesian methodology that we call \textit{Good's Principle}: one should always delay making a terminal decision between alternative courses of action if given the opportunity to first learn, at zero cost, the outcome of an experiment relevant to the decision. In particular, both dilation and non-conglomerability have been alleged to permit or even mandate choosing to make a terminal decision in deliberate ignorance of relevant, cost-free information. Although (...)
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