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  1. Coping Rationally with Ambiguity: Robustness Versus Ambiguity-Aversion: Klaus Nehring.Klaus Nehring - 2009 - Economics and Philosophy 25 (3):303-334.
    Al-Najjar and Weinstein argue that the extant literature on ambiguity aversion is not successful in accounting for Ellsberg choices as rational responses to ambiguity. We concur, and propose that rational choice under ambiguity aims at robustness rather than avoidance of ambiguity. A central argument explains why robust choice is intrinsically context-dependent and legitimately violates standard choice consistency conditions. If choice consistency is forced, however, ambiguity-aversion emerges as a semi-rational response to ambiguity.
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  • Resurrecting Logical Probability.J. Franklin - 2001 - Erkenntnis 55 (2):277-305.
    The logical interpretation of probability, or "objective Bayesianism'' – the theory that (some) probabilities are strictly logical degrees of partial implication – is defended. The main argument against it is that it requires the assignment of prior probabilities, and that any attempt to determine them by symmetry via a "principle of insufficient reason" inevitably leads to paradox. Three replies are advanced: that priors are imprecise or of little weight, so that disagreement about them does not matter, within limits; that it (...)
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  • A Subjectivist’s Guide to Deterministic Chance.J. Dmitri Gallow - 2021 - Synthese 198 (5):4339-4372.
    I present an account of deterministic chance which builds upon the physico-mathematical approach to theorizing about deterministic chance known as 'the method of arbitrary functions'. This approach promisingly yields deterministic probabilities which align with what we take the chances to be---it tells us that there is approximately a 1/2 probability of a spun roulette wheel stopping on black, and approximately a 1/2 probability of a flipped coin landing heads up---but it requires some probabilistic materials to work with. I contend that (...)
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  • David Makinson on Classical Methods for Non-Classical Problems.Sven Ove Hansson (ed.) - 2013 - Dordrecht, Netherland: Springer.
    The volume analyses and develops David Makinson’s efforts to make classical logic useful outside its most obvious application areas. The book contains chapters that analyse, appraise, or reshape Makinson’s work and chapters that develop themes emerging from his contributions. These are grouped into major areas to which Makinsons has made highly influential contributions and the volume in its entirety is divided into four sections, each devoted to a particular area of logic: belief change, uncertain reasoning, normative systems and the resources (...)
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  • Challenges to Bayesian Confirmation Theory.John D. Norton - 2011 - In Prasanta S. Bandyopadhyay & Malcolm R. Forster (eds.), Handbook of the Philosophy of Science, Vol. 7: Philosophy of Statistics. Elsevier B.V.. pp. 391-440.
    Proponents of Bayesian confirmation theory believe that they have the solution to a significant, recalcitrant problem in philosophy of science. It is the identification of the logic that governs evidence and its inductive bearing in science. That is the logic that lets us say that our catalog of planetary observations strongly confirms Copernicus’ heliocentric hypothesis; or that the fossil record is good evidence for the theory of evolution; or that the 3oK cosmic background radiation supports big bang cosmology. The definitive (...)
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  • The Interpretation of Uncertainty in Ecological Rationality.Anastasia Kozyreva & Ralph Hertwig - 2019 - Synthese 198 (2):1517-1547.
    Despite the ubiquity of uncertainty, scientific attention has focused primarily on probabilistic approaches, which predominantly rely on the assumption that uncertainty can be measured and expressed numerically. At the same time, the increasing amount of research from a range of areas including psychology, economics, and sociology testify that in the real world, people’s understanding of risky and uncertain situations cannot be satisfactorily explained in probabilistic and decision-theoretical terms. In this article, we offer a theoretical overview of an alternative approach to (...)
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  • Getting Accurate About Knowledge.Sam Carter & Simon Goldstein - 2022 - Mind:1-28.
    There is a large literature exploring how accuracy constrains rational degrees of belief. This paper turns to the unexplored question of how accuracy constrains knowledge. We begin by introducing a simple hypothesis: increases in the accuracy of an agent’s evidence never lead to decreases in what the agent knows. We explore various precise formulations of this principle, consider arguments in its favor, and explain how it interacts with different conceptions of evidence and accuracy. As we show, the principle has some (...)
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  • Against Radical Credal Imprecision.Susanna Rinard - 2013 - Thought: A Journal of Philosophy 2 (1):157-165.
    A number of Bayesians claim that, if one has no evidence relevant to a proposition P, then one's credence in P should be spread over the interval [0, 1]. Against this, I argue: first, that it is inconsistent with plausible claims about comparative levels of confidence; second, that it precludes inductive learning in certain cases. Two motivations for the view are considered and rejected. A discussion of alternatives leads to the conjecture that there is an in-principle limitation on formal representations (...)
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  • Intuitive Dilation?Casey Hart & Michael G. Titelbaum - 2015 - Thought: A Journal of Philosophy 4 (4):252-262.
    Roger White objects to interval-valued credence theories because they produce a counterintuitive “dilation” effect in a story he calls the Coin Game. We respond that results in the Coin Game were bound to be counterintuitive anyway, because the story involves an agent who learns a biconditional. Biconditional updates produce surprising results whether the credences involved are ranged or precise, so White's story is no counterexample to ranged credence theories.
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  • Indifference to Anti-Humean Chances.J. Dmitri Gallow - forthcoming - Canadian Journal of Philosophy.
    An indifference principle says that your credences should be distributed uniformly over each of the possibilities you recognise. A chance deference principle says that your credences should be aligned with the chances. My thesis is that, if we are anti-Humeans about chance, then these two principles are incompatible. Anti-Humeans think that it is possible for the actual frequencies to depart from the chances. So long as you recognise possibilities like this, you cannot both spread your credences evenly and defer to (...)
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  • Distention for Sets of Probabilities.Rush T. Stewart & Michael Nielsen - 2022 - Philosophy of Science 89 (3):604-620.
    Bayesians often appeal to “merging of opinions” to rebut charges of excessive subjectivity. But what happens in the short run is often of greater interest than what happens in the limit. Seidenfeld and coauthors use this observation as motivation for investigating the counterintuitive short run phenomenon of dilation, since, they allege, dilation is “the opposite” of asymptotic merging of opinions. The measure of uncertainty relevant for dilation, however, is not the one relevant for merging of opinions. We explicitly investigate the (...)
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  • Probabilistic Logics and Probabilistic Networks.Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson - 2010 - Dordrecht, Netherland: Synthese Library.
    Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.
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  • Sleeping Beauty and the Forgetful Bayesian.Bradley Monton - 2002 - Analysis 62 (1):47–53.
    Adam Elga takes the Sleeping Beauty example to provide a counter-example to Reflection, since on Sunday Beauty assigns probability 1/2 to H, and she is certain that on Monday she will assign probability 1/3. I will show that there is a natural way for Bas van Fraassen to defend Reflection in the case of Sleeping Beauty, building on van Fraassen’s treatment of forgetting. This will allow me to identify a lacuna in Elga’s argument for 1/3. I will then argue, however, (...)
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  • Using Imprecise Probabilities to Address the Questions of Inference and Decision in Randomized Clinical Trials.Lyle C. Gurrin, Peter D. Sly & Paul R. Burton - 2002 - Journal of Evaluation in Clinical Practice 8 (2):255-268.
    Randomized controlled clinical trials play an important role in the development of new medical therapies. There is, however, an ethical issue surrounding the use of randomized treatment allocation when the patient is suffering from a life threatening condition and requires immediate treatment. Such patients can only benefit from the treatment they actually receive and not from the alternative therapy, even if it ultimately proves to be superior. We discuss a novel new way to analyse data from such clinical trials based (...)
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  • Degrees of Belief.Franz Huber & Christoph Schmidt-Petri (eds.) - 2008 - Dordrecht and Heidelberg: Springer.
    Various theories try to give accounts of how measures of this confidence do or ought to behave, both as far as the internal mental consistency of the agent as ...
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  • Making Climate Decisions.Richard Bradley & Katie Steele - 2015 - Philosophy Compass 10 (11):799-810.
    Many fine-grained decisions concerning climate change involve significant, even severe, uncertainty. Here, we focus on modelling the decisions of single agents, whether individual persons or groups perceived as corporate entities. We offer a taxonomy of the sources and kinds of uncertainty that arise in framing these decision problems, as well as strategies for making a choice in spite of uncertainty. The aim is to facilitate a more transparent and structured treatment of uncertainty in climate decision making.
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  • Proper Scoring Rules in Epistemic Decision Theory.Maomei Wang - 2020 - Dissertation, Lingnan University
    Epistemic decision theory aims to defend a variety of epistemic norms in terms of their facilitation of epistemic ends. One of the most important components of EpDT is known as a scoring rule. This thesis addresses some problems about scoring rules in EpDT. I consider scoring rules both for precise credences and for imprecise credences. For scoring rules in the context of precise credences, I examine the rationale for requiring a scoring rule to be strictly proper, and argue that no (...)
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  • Bayesian Epistemology.William Talbott - 2006 - Stanford Encyclopedia of Philosophy.
    ‘Bayesian epistemology’ became an epistemological movement in the 20th century, though its two main features can be traced back to the eponymous Reverend Thomas Bayes (c. 1701-61). Those two features are: (1) the introduction of a formal apparatus for inductive logic; (2) the introduction of a pragmatic self-defeat test (as illustrated by Dutch Book Arguments) for epistemic rationality as a way of extending the justification of the laws of deductive logic to include a justification for the laws of inductive logic. (...)
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  • Decision Theory.Katie Steele & H. Orri Stefánsson - 2015 - In Stanford Encyclopedia of Philosophy.
  • Formal Representations of Belief.Franz Huber - 2008 - Stanford Encyclopedia of Philosophy.
    Epistemology is the study of knowledge and justified belief. Belief is thus central to epistemology. It comes in a qualitative form, as when Sophia believes that Vienna is the capital of Austria, and a quantitative form, as when Sophia's degree of belief that Vienna is the capital of Austria is at least twice her degree of belief that tomorrow it will be sunny in Vienna. Formal epistemology, as opposed to mainstream epistemology (Hendricks 2006), is epistemology done in a formal way, (...)
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  • Imprecise Probabilities.Seamus Bradley - 2019 - Stanford Encyclopedia of Philosophy.
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  • Consequences of Comparability.Cian Dorr, Jacob M. Nebel & Jake Zuehl - 2021 - Philosophical Perspectives 35 (1):70-98.
    We defend three controversial claims about preference, credence, and choice. First, all agents (not just rational ones) have complete preferences. Second, all agents (again, not just rational ones) have real-valued credences in every proposition in which they are confident to any degree. Third, there is almost always some unique thing we ought to do, want, or believe.
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  • Being Realist About Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - 2021 - British Journal for the Philosophy of Science 72 (1):185-220.
    Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...)
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  • One Philosopher's Modus Ponens is Another's Modus Tollens: Pantomemes and Nisowir.Jon Williamson - 2022 - Metaphilosophy 53 (2-3):284-304.
    Metaphilosophy, Volume 53, Issue 2-3, Page 284-304, April 2022.
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  • Credal Imprecision and the Value of Evidence.Nilanjan Das - forthcoming - Noûs.
    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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  • Safe Contraction Revisited.Hans Rott & Sven Ove Hansson - 2014 - In Sven Ove Hansson (ed.), David Makinson on Classical Methods for Non-Classical Problems (Outstanding Contributions to Logic, Vol. 3). Dordrecht: Springer. pp. 35–70.
    Modern belief revision theory is based to a large extent on partial meet contraction that was introduced in the seminal article by Carlos Alchourrón, Peter Gärdenfors, and David Makinson that appeared in 1985. In the same year, Alchourrón and Makinson published a significantly different approach to the same problem, called safe contraction. Since then, safe contraction has received much less attention than partial meet contraction. The present paper summarizes the current state of knowledge on safe contraction, provides some new results (...)
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  • Dilation and Asymmetric Relevance.Arthur Paul Pedersen & Gregory Wheeler - 2019 - Proceedings of Machine Learning Research 103:324-26.
    A characterization result of dilation in terms of positive and negative association admits an extremal counterexample, which we present together with a minor repair of the result. Dilation may be asymmetric whereas covariation itself is symmetric. Dilation is still characterized in terms of positive and negative covariation, however, once the event to be dilated has been specified.
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  • Discounting Desirable Gambles.Gregory Wheeler - 2021 - Proceedings of Machine Learning Research 147:331-341.
    The desirable gambles framework offers the most comprehensive foundations for the theory of lower pre- visions, which in turn affords the most general ac- count of imprecise probabilities. Nevertheless, for all its generality, the theory of lower previsions rests on the notion of linear utility. This commitment to linearity is clearest in the coherence axioms for sets of desirable gambles. This paper considers two routes to relaxing this commitment. The first preserves the additive structure of the desirable gambles framework and (...)
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  • Moving Beyond Sets of Probabilities.Gregory Wheeler - 2021 - Statistical Science 36 (2):201--204.
    The theory of lower previsions is designed around the principles of coherence and sure-loss avoidance, thus steers clear of all the updating anomalies highlighted in Gong and Meng's "Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox" except dilation. In fact, the traditional problem with the theory of imprecise probability is that coherent inference is too complicated rather than unsettling. Progress has been made simplifying coherent inference by demoting sets of probabilities from fundamental building blocks to secondary representations (...)
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  • The Problem of Granularity for Scientific Explanation.David Kinney - 2019 - Dissertation, London School of Economics and Political Science (LSE)
    This dissertation aims to determine the optimal level of granularity for the variables used in probabilistic causal models. These causal models are useful for generating explanations in a number of scientific contexts. In Chapter 1, I argue that there is rarely a unique level of granularity at which a given phenomenon can be causally explained, thereby rejecting various causal exclusion arguments. In Chapter 2, I consider several recent proposals for measuring the explanatory power of causal explanations, and show that these (...)
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  • What is the Normative Role of Logic?Peter Milne - 2009 - Aristotelian Society Supplementary Volume 83 (1):269-298.
    In making assertions one takes on commitments to the consistency of what one asserts and to the logical consequences of what one asserts. Although there is no quick link between belief and assertion, the dialectical requirements on assertion feed back into normative constraints on those beliefs that constitute one's evidence. But if we are not certain of many of our beliefs and that uncertainty is modelled in terms of probabilities, then there is at least prima facie incoherence between the normative (...)
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  • Peter Fishburn’s Analysis of Ambiguity.Mark Shattuck & Carl Wagner - 2016 - Theory and Decision 81 (2):153-165.
    In ordinary discourse the term ambiguity typically refers to vagueness or imprecision in a natural language. Among decision theorists, however, this term usually refers to imprecision in an individual’s probabilistic judgments, in the sense that the available evidence is consistent with more than one probability distribution over possible states of the world. Avoiding a prior commitment to either of these interpretations, Fishburn has explored ambiguity as a primitive concept, in terms of what he calls an ambiguity measure a on the (...)
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  • Imprecise Bayesian Networks as Causal Models.David Kinney - 2018 - Information 9 (9):211.
    This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability (...)
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  • How to Be an Imprecise Impermissivist.Seamus Bradley - manuscript
    Rational credence should be coherent in the sense that your attitudes should not leave you open to a sure loss. Rational credence should be such that you can learn when confronted with relevant evidence. Rational credence should not be sensitive to irrelevant differences in the presentation of the epistemic situation. We explore the extent to which orthodox probabilistic approaches to rational credence can satisfy these three desiderata and find them wanting. We demonstrate that an imprecise probability approach does better. Along (...)
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  • Learning by Ignoring the Most Wrong.Seamus Bradley - 2022 - Kriterion – Journal of Philosophy 36 (1):9-31.
    Imprecise probabilities are an increasingly popular way of reasoning about rational credence. However they are subject to an apparent failure to display convincing inductive learning. This paper demonstrates that a small modification to the update rule for IP allows us to overcome this problem, albeit at the cost of satisfying only a weaker concept of coherence.
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  • Subjective Probability as Sampling Propensity.Thomas Icard - 2016 - Review of Philosophy and Psychology 7 (4):863-903.
    Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...)
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  • The Psychology of Dynamic Probability Judgment: Order Effect, Normative Theories, and Experimental Methodology.Jean Baratgin & Guy Politzer - 2007 - Mind and Society 6 (1):53-66.
    The Bayesian model is used in psychology as the reference for the study of dynamic probability judgment. The main limit induced by this model is that it confines the study of revision of degrees of belief to the sole situations of revision in which the universe is static (revising situations). However, it may happen that individuals have to revise their degrees of belief when the message they learn specifies a change of direction in the universe, which is considered as changing (...)
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  • The Coherence Argument Against Conditionalization.Matthias Hild - 1998 - Synthese 115 (2):229-258.
    I re-examine Coherence Arguments (Dutch Book Arguments, No Arbitrage Arguments) for diachronic constraints on Bayesian reasoning. I suggest to replace the usual game–theoretic coherence condition with a new decision–theoretic condition ('Diachronic Sure Thing Principle'). The new condition meets a large part of the standard objections against the Coherence Argument and frees it, in particular, from a commitment to additive utilities. It also facilitates the proof of the Converse Dutch Book Theorem. I first apply the improved Coherence Argument to van Fraassen's (...)
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  • Coherent Probability From Incoherent Judgment.Daniel Osherson, David Lane, Peter Hartley & Richard R. Batsell - 2001 - Journal of Experimental Psychology: Applied 7 (1):3.
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  • Imprecise Bayesianism and Inference to the Best Explanation.Namjoong Kim - forthcoming - Foundations of Science:1-27.
    According to van Fraassen, inference to the best explanation is incompatible with Bayesianism. To argue to the contrary, many philosophers have suggested hybrid models of scientific reasoning with both explanationist and probabilistic elements. This paper offers another such model with two novel features. First, its Bayesian component is imprecise. Second, the domain of credence functions can be extended.
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  • The Bayesian Who Knew Too Much.Yann Benétreau-Dupin - 2015 - Synthese 192 (5):1527-1542.
    In several papers, John Norton has argued that Bayesianism cannot handle ignorance adequately due to its inability to distinguish between neutral and disconfirming evidence. He argued that this inability sows confusion in, e.g., anthropic reasoning in cosmology or the Doomsday argument, by allowing one to draw unwarranted conclusions from a lack of knowledge. Norton has suggested criteria for a candidate for representation of neutral support. Imprecise credences (families of credal probability functions) constitute a Bayesian-friendly framework that allows us to avoid (...)
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  • Reason and the Grain of Belief.Scott Sturgeon - 2008 - Noûs 42 (1):139–165.
  • Risk, Uncertainty and Hidden Information.Stephen Morris - 1997 - Theory and Decision 42 (3):235-269.
    People are less willing to accept bets about an event when they do not know the true probability of that event. Such uncertainty aversion has been used to explain certain economic phenomena. This paper considers how far standard private information explanations (with strategic decisions to accept bets) can go in explaining phenomena attributed to uncertainty aversion. This paper shows that if two individuals have different prior beliefs about some event, and two sided private information, then each individual’s willingness to bet (...)
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  • Scoring Imprecise Credences: A Mildly Immodest Proposal.Conor Mayo-Wilson & Gregory Wheeler - 2016 - Philosophy and Phenomenological Research 92 (1):55-78.
    Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, we employ a (slightly-generalized) impossibility theorem of Seidenfeld, Schervish, and Kadane (2012), who show that (...)
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  • Bayesian Statistics in Medical Research: An Intuitive Alternative to Conventional Data Analysis.Lyle C. Gurrin, Jennifer J. Kurinczuk & Paul R. Burton - 2000 - Journal of Evaluation in Clinical Practice 6 (2):193-204.
  • Resolving Peer Disagreements Through Imprecise Probabilities.Lee Elkin & Gregory Wheeler - 2018 - Noûs 52 (2):260-278.
    Two compelling principles, the Reasonable Range Principle and the Preservation of Irrelevant Evidence Principle, are necessary conditions that any response to peer disagreements ought to abide by. The Reasonable Range Principle maintains that a resolution to a peer disagreement should not fall outside the range of views expressed by the peers in their dispute, whereas the Preservation of Irrelevant Evidence Principle maintains that a resolution strategy should be able to preserve unanimous judgments of evidential irrelevance among the peers. No standard (...)
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  • Comparative Expectations.Arthur Paul Pedersen - 2014 - Studia Logica 102 (4):811-848.
    I introduce a mathematical account of expectation based on a qualitative criterion of coherence for qualitative comparisons between gambles (or random quantities). The qualitative comparisons may be interpreted as an agent’s comparative preference judgments over options or more directly as an agent’s comparative expectation judgments over random quantities. The criterion of coherence is reminiscent of de Finetti’s quantitative criterion of coherence for betting, yet it does not impose an Archimedean condition on an agent’s comparative judgments, it does not require the (...)
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  • The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence.Carlotta Piscopo & Mauro Birattari - 2008 - Minds and Machines 18 (2):273-288.
    In artificial intelligence (AI), a number of criticisms were raised against the use of probability for dealing with uncertainty. All these criticisms, except what in this article we call the non-adequacy claim, have been eventually confuted. The non-adequacy claim is an exception because, unlike the other criticisms, it is exquisitely philosophical and, possibly for this reason, it was not discussed in the technical literature. A lack of clarity and understanding of this claim had a major impact on AI. Indeed, mostly (...)
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  • Non-Measurability, Imprecise Credences, and Imprecise Chances.Yoaav Isaacs, Alan Hájek & John Hawthorne - forthcoming - Mind:fzab031.
    We offer a new motivation for imprecise probabilities. We argue that there are propositions to which precise probability cannot be assigned, but to which imprecise probability can be assigned. In such cases the alternative to imprecise probability is not precise probability, but no probability at all. And an imprecise probability is substantially better than no probability at all. Our argument is based on the mathematical phenomenon of non-measurable sets. Non-measurable propositions cannot receive precise probabilities, but there is a natural way (...)
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  • Sleeping Beauty, Countable Additivity, and Rational Dilemmas.Jacob Ross - 2010 - Philosophical Review 119 (4):411-447.
    Currently, the most popular views about how to update de se or self-locating beliefs entail the one-third solution to the Sleeping Beauty problem.2 Another widely held view is that an agent‘s credences should be countably additive.3 In what follows, I will argue that there is a deep tension between these two positions. For the assumptions that underlie the one-third solution to the Sleeping Beauty problem entail a more general principle, which I call the Generalized Thirder Principle, and there are situations (...)
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