Most philosophers of science hold that the laws of nature play an important role in determining which counterfactuals are true. Marc Lange reverses this dependence, arguing that it is the truth of certain counterfactuals that determines which statements are laws. I argue that the context sensitivity of counterfactual sentences makes it impossible for them to determine the laws. Next, I argue that Lange's view cannot avoid additional counterexamples concerning nested counterfactuals. Finally, I argue that Lange's counterfacts, posited as the ultimate ontological ground for the laws of nature, are unsuited to the role he demands of them.

Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: (1) robustness analysis may be of limited or no value in evaluating models and their implications; (2) the unificatory reconstruction conceals that the three types of robustness differ in form and role; (3) there is no confluence of types of robustness. We illustrate our central first claim with a case study: the application of Lotka-Volterra models to technology diffusion.

We discuss the probabilistic analysis of explanatory power and prove a representation theorem for posterior ratio measures recently advocated by Schupbach and Sprenger. We then prove a representation theorem for an alternative class of measures that rely on the notion of relative probability distance. We end up endorsing the latter, as relative distance measures share the properties of posterior ratio measures that are genuinely appealing, while overcoming a feature that we consider undesirable. They also yield a telling result concerning formal accounts of explanatory power versus inductive confirmation, thereby bridging our discussion to a so-called no-miracle argument.

The article discusses a well-known type of counterexample to Causal Decision Theory (CDT), in which CDT recommends an option that probably causes the best outcome while itself being evidence that it causes the worst. Intuition disagrees. Many philosophers accept that this justifies either modifying CDT or dropping it altogether. I argue to the contrary that (a) if intuition is right about this case, then transitivity of preference must be violated in another, but (b) this violation is untenable. I conclude that CDT stands.

On the classical account of evolution by natural selection (ENS) found in Lewontin and many subsequent authors, ENS is conceived as involving three key ingredients: phenotypic variation, fitness differences, and heredity. Through the analysis of three problem cases involving heredity, I argue that the classical conception is substantially flawed, showing that heredity is not required for selection. I consider further problems with the classical account of ENS arising from conflations between three distinct senses of the central concept of ‘fitness’ and offer an alternative to the classical conception of ENS involving the interaction of distinct evolutionary mechanisms.

Leitgeb and Pettigrew argue that (1) agents should minimize the expected inaccuracy of their beliefs and (2) inaccuracy should be measured via the Brier score. They show that in certain diachronic cases, these claims require an alternative to Jeffrey Conditionalization. I claim that this alternative is an irrational updating procedure and that the Brier score, and quadratic scoring rules generally, should be rejected as legitimate measures of inaccuracy.