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Olav Benjamin Vassend [6]Olav B. Vassend [4]
  1.  38
    Why Change Your Beliefs Rather Than Your Desires? Two Puzzles.Olav Benjamin Vassend - 2021 - Analysis 81 (2):275-281.
    In standard decision theory, the probability function ought to be updated in light of evidence, but the utility function generally stays fixed. However, there is nothing in the formal theory that prevents one from instead updating the utility function, while keeping the probability function fixed. Moreover, there are good arguments for updating the utilities and not just the probabilities. Hence, the first puzzle is whether there is anything that justifies updating beliefs, but not desires, in light of evidence. The paper (...)
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  2.  36
    New Semantics for Bayesian Inference: The Interpretive Problem and Its Solutions.Olav Benjamin Vassend - 2019 - Philosophy of Science 86 (4):696-718.
    Scientists often study hypotheses that they know to be false. This creates an interpretive problem for Bayesians because the probability assigned to a hypothesis is typically interpreted as the probability that the hypothesis is true. I argue that solving the interpretive problem requires coming up with a new semantics for Bayesian inference. I present and contrast two new semantic frameworks, and I argue that both of them support the claim that there is pervasive pragmatic encroachment on whether a given Bayesian (...)
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  3.  17
    A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Olav B. Vassend - 2020 - British Journal for the Philosophy of Science 71 (4):1359-1383.
    Bayesianism and likelihoodism are two of the most important frameworks philosophers of science use to analyse scientific methodology. However, both frameworks face a serious objection: much scientific inquiry takes place in highly idealized frameworks where all the hypotheses are known to be false. Yet, both Bayesianism and likelihoodism seem to be based on the assumption that the goal of scientific inquiry is always truth rather than closeness to the truth. Here, I argue in favour of a verisimilitude framework for inductive (...)
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  4.  23
    Confirmation and the Ordinal Equivalence Thesis.Olav Benjamin Vassend - 2017 - Synthese:1-17.
    According to a widespread but implicit thesis in Bayesian confirmation theory, two confirmation measures are considered equivalent if they are ordinally equivalent—call this the “ordinal equivalence thesis”. I argue that adopting OET has significant costs. First, adopting OET renders one incapable of determining whether a piece of evidence substantially favors one hypothesis over another. Second, OET must be rejected if merely ordinal conclusions are to be drawn from the expected value of a confirmation measure. Furthermore, several arguments and applications of (...)
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  5.  35
    Justifying the Norms of Inductive Inference.Olav Benjamin Vassend - 2022 - British Journal for the Philosophy of Science 73 (1):135-160.
    Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favouring, even when that is inappropriate. The purpose of this article is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favouring is not necessarily the likelihood. I (...)
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  6.  33
    Confirmation Measures and Sensitivity.Olav B. Vassend - 2015 - Philosophy of Science 82 (5):892-904.
    Stanley Stevens draws a useful distinction among ordinal scales, interval scales, and ratio scales. Most recent discussions of confirmation measures have proceeded on the ordinal level of analysis. In this article, I give a more quantitative analysis. In particular, I show that the requirement that our desired confirmation measure be at least an interval measure naturally yields necessary conditions that jointly entail the log-likelihood measure. Thus, I conclude that the log-likelihood measure is the only good candidate interval measure.
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  7.  8
    Review of Bayesian Philosophy of Science. [REVIEW]Olav Benjamin Vassend - forthcoming - Erkenntnis:1-5.
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  8.  18
    Goals and the Informativeness of Prior Probabilities.Olav Benjamin Vassend - 2017 - Erkenntnis:1-24.
    I argue that information is a goal-relative concept for Bayesians. More precisely, I argue that how much information is provided by a piece of evidence depends on whether the goal is to learn the truth or to rank actions by their expected utility, and that different confirmation measures should therefore be used in different contexts. I then show how information measures may reasonably be derived from confirmation measures, and I show how to derive goal-relative non-informative and informative priors given background (...)
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  9.  12
    Bayesian Statistical Inference and Approximate Truth.Olav B. Vassend - unknown
    Scientists and Bayesian statisticians often study hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis is supposed to represent the probability that the hypothesis is true. I investigate whether Bayesianism can accommodate the idea that false hypotheses are sometimes approximately true or that some hypotheses or models can be closer to the truth than others. I argue that the idea that some hypotheses are approximately true in an absolute sense is (...)
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  10.  12
    Nonstandard Bayesianism: How Verisimilitude and Counterfactual Degrees of Belief Solve the Interpretive Problem in Bayesian Inference.Olav B. Vassend - unknown
    Scientists and Bayesian statisticians often study hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis is typically interpreted as a degree of belief that the hypothesis is true. In this paper, I present and contrast two solutions to the interpretive problem, both of which involve reinterpreting the Bayesian framework in such a way that pragmatic factors directly determine in part how probability assignments are interpreted and whether a given probability assignment (...)
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