Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic values can play a legitimate role as factors that override epistemic considerations in assessing scientific representations for practical purposes.

Frank has recently argued for an information-theoretic interpretation of natural selection. This interpretation is based on the identification of a measure related to the Malthusian parameter (for population change) with the Jeffreys divergence between the present allelic distribution of the population and that distribution in the next generation. It is pointed out in this analysis that this identification only holds if the mean fitness of the population is a constant, that is, there is no selection. This problem is used to argue for the superiority of the standard dynamical interpretation of natural selection over its information-theoretic counterpart.

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC.

Some philosophers respond to Leibniz’s “shift” argument against absolute space by appealing to antihaecceitism about possible worlds, using David Lewis’s counterpart theory. But separated from Lewis’s distinctive system, it is difficult to understand what this doctrine amounts to or how it bears on the Leibnizian argument. In fact, the best way of making sense of the relevant kind of antihaecceitism concedes the main point of the Leibnizian argument, pressing us to consider alternative spatiotemporal metaphysics.

The canonical Bayesian solution to the ravens paradox faces a problem: it entails that black nonravens disconfirm the hypothesis that all ravens are black. I provide a new solution that avoids this problem. On my solution, black ravens confirm that all ravens are black, while nonblack nonravens and black nonravens are neutral. My approach is grounded in certain relations of epistemic dependence, which, in turn, are grounded in the fact that the kind raven is more natural than the kind black. The solution applies to any generalization “All F’s are G” in which F is more natural than G.

I show how scoring rules, interpreted as measuring the inaccuracy of a set of degrees of belief, may be exploited to construct confirmation measures as used in Bayesian confirmation theory. I construct two confirmation measures from two particular standard scoring rules. One of these measures is genuinely new, the second is trivially ordinally equivalent to the difference measure. These two measures are tested against three well-known measures of confirmation in a simple but illuminating case that contains in a natural way the problem of irrelevant conjunction. The genuinely new measure emerges, arguably, as the best.

We show that as a chain of confirmation becomes longer, confirmation dwindles under screening-off. For example, if E confirms H1, H1 confirms H2, and H1 screens off E from H2, then the degree to which E confirms H2 is less than the degree to which E confirms H1. Although there are many measures of confirmation, our result holds on any measure that satisfies the Weak Law of Likelihood. We apply our result to testimony cases, relate it to the Data-Processing Inequality in information theory, and extend it in two respects so that it covers a broader range of cases.

In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the phenomena the mechanism brings about. Finally, I sketch an alternative approach taking (i) and (ii) into account.

In a recent article in Philosophy of Science, De Vignemont and Jacob defend the view that empathy involves interpersonal similarity between an empathizer and a target person with respect to internal affective states. Focusing on empathy for pain, they propose a theory of the neural substrate of pain empathy. We point out several flaws in their interpretation of the data and argue that currently available data do not differentiate between De Vignemont and Jacob’s model and alternative models. Finally, we offer some suggestions about how this might be achieved in future research.