• The British Journal for the Philosophy of Science | Vol 75, No 2
    British Journal for the Philosophy of Science 71 (4): 1359-1383. 2018.
  •  15
    What hinge epistemology and Bayesian epistemology can learn from each other
    Asian Journal of Philosophy 2 (2): 1-21. 2023.
    Hinge epistemology and Bayesianism are two prominent approaches in contemporary epistemology, but the relationship between these approaches has not been systematically studied. This paper formalizes the central commitments of hinge epistemology in a Bayesian framework and argues for the following two theses: (1) many of the types of claims that are treated as paradigmatic hinges in the hinge epistemology literature, such as the claim that there exists an external world of physical objects, are n…Read more
  •  79
    Sometimes It Is Better to Do Nothing: A New Argument for Causal Decision Theory
    Ergo: An Open Access Journal of Philosophy 9 (n/a). 2022.
    It is often thought that the main significant difference between evidential decision theory and causal decision theory is that they recommend different acts in Newcomb-style examples (broadly construed) where acts and states are correlated in peculiar ways. However, this paper presents a class of non-Newcombian examples that evidential decision theory cannot adequately model whereas causal decision theory can. Briefly, the examples involve situations where it is clearly best to perform an act th…Read more
  •  82
    The philosophical significance of Stein’s paradox
    European Journal for Philosophy of Science 7 (3): 411-433. 2017.
    Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importance. Here we explore its philosophical implications. We outline the nature of Stein’s result and of subsequent work on shrinkage estimators; then we describe how these results are related to Bayesianism and to model selection criteria like AIC. We also discuss their bearing on scientific realism and instrumentalism. We argue that results concerning shrinkage estimators underwrite a surprising form o…Read more
  •  73
    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 l…Read more
  •  84
    Justifying the Norms of Inductive Inference
    British Journal for the Philosophy of Science 73 (1): 135-160. 2022.
    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 us…Read more
  •  23
    Review of Bayesian Philosophy of Science (review)
    Erkenntnis 88 (5): 2245-2249. 2023.
  •  52
    Confirmation Measures and Sensitivity
    Philosophy of Science 82 (5): 892-904. 2015.
    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 …Read more
  •  41
    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 conf…Read more
  •  56
    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 wheth…Read more
  •  34
    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 p…Read more
  •  33
    Confirmation and the ordinal equivalence thesis
    Synthese 196 (3): 1079-1095. 2019.
    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” (OET). 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 …Read more
  •  42
    A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics
    British Journal for the Philosophy of Science 71 (4): 1359-1383. 2018.
    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 verisimi…Read more
  •  27
    Goals and the Informativeness of Prior Probabilities
    Erkenntnis 83 (4): 647-670. 2018.
    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 p…Read more
  •  182
    One of the main goals of Bayesian epistemology is to justify the rational norms credence functions ought to obey. Accuracy arguments attempt to justify these norms from the assumption that the source of value for credences relevant to their epistemic status is their accuracy. This assumption and some standard decision-theoretic principles are used to argue for norms like Probabilism, the thesis that an agent’s credence function is rational only if it obeys the probability axioms. We introduce an…Read more