This article argues for a representational semantic conception (RSC) of scientific theories, which respects the bare claim of any semantic view, namely, that theories can be characterized as sets of models. RSC must be sharply distinguished from structural versions that assume a further identity of ‘models’ and ‘structures’, which we reject. The practice turn in the recent philosophical literature suggests instead that modeling must be understood in a deflationary spirit, in terms of the diverse representational practices in the sciences. These insights are applied to some mathematical models, thus showing that the mathematical sciences are not in principle counterexamples to RSC.

This is one of a pair of discussion notes comparing some features of the account of causation in Wolfgang Spohn’s Laws of Belief with the “interventionist” account in James Woodward’s Making Things Happen. Despite striking similarities there are also important differences. These include (i) the “epistemic” orientation of Spohn’s account as opposed to the worldly or “ontic” orientation of the interventionist account, (ii) Spohn’s focus on token-level causal claims in contrast to the primary interventionist focus on type-level claims, (iii) the role of temporal information in accounting for causal asymmetry, and (iv) the extent to which causal claims are “frame relative.”

According to Skow, correct answers to why questions cite only causes or grounds, but not nonaccidental regularities. Accounts that cite nonaccidental regularities typically confuse second-level reasons with first-level reasons. Only causes and grounds are first-level reasons why. Nonaccidental regularities are second-level reasons why. I first show that Skow’s arguments for the accusation of confusion depend on the independent thesis that only citations of first-level reasons why are (parts of) answers to why questions. Then I argue that this thesis is false. Consequently, the claim that correct answers to why questions cite only causes or grounds is refuted as well.

The Pessimistic Induction over the history of science argues that because most past theories judged empirically successful in their time are not even approximately true, most present ones probably are not approximately true either. But why did past scientists accept those incorrect theories? Kyle Stanford’s Problem of Unconceived Alternatives is one answer to that question: scientists are bad at exhausting the space of plausible hypotheses to explain the available evidence. Here, I offer another answer: the Problem of Misleading Evidence. I argue that this proposal enjoys some advantages over Stanford’s, although both are probably needed to explain the historical record.

This is one of a pair of discussion notes comparing some features of the account of causation in Wolfgang Spohn’s Laws of Belief with the “interventionist” account in James Woodward’s Making Things Happen. This note locates the core difference of the accounts in the fact that Woodward’s account follows an epistemological order, while Spohn’s follows a conceptual order. This unfolds in five further differences: (i) type- versus token-level causation, (ii) reference to time, (iii) actual/counterfactual intervention versus epistemic/suppositional wiggling, (iv) a circular versus a circle-free conception of the circumstances of a direct causal relation, and (v) absolute versus model-relative causation.

Timothy Williamson claimed to prove with a coin-tossing example that hyperreal probabilities cannot save the principle of regularity. A premise of his argument is that two specified infinitary events must be assigned the same probability because, he claims, they are isomorphic. But as has been pointed out, they are not isomorphic. A way of framing Williamson’s argument that does not make it depend on the isomorphism claim is in terms of shifts in Bernoulli processes, the usual mathematical model of sequential coin tossing. But even so framed, the argument still fails.

Historical inductions, that is, the pessimistic metainduction and the problem of unconceived alternatives, are critically analyzed via John D. Norton’s material theory of induction and subsequently rejected as noncogent arguments. It is suggested that the material theory is amenable to a local version of the pessimistic metainduction, for example, in the context of some medical studies.

A number of formal models, including a highly influential model from Hong and Page, purport to show that functionally diverse groups often beat groups of individually high-performing agents in solving problems. Thompson argues that in Hong and Page’s model, that the diverse groups are created by a random process explains their success, not the diversity. Here, I defend the diversity interpretation of the Hong and Page result. The failure of Thompson’s argument shows that to understand the value of functional diversity, we should be clearer about how we conceive of and measure that diversity.

Charlotte Werndl and Roman Frigg discuss the relationship between the Boltzmannian and Gibbsian framework of statistical mechanics, addressing, in particular, the question when equilibrium values calculated in both frameworks agree. This note points out conceptual confusions that could arise from their discussion, concerning, in particular, the authors’ use of “Boltzmann equilibrium.” It also clarifies the status of the Khinchin condition for the equivalence of Boltzmannian and Gibbsian equilibrium predictions and shows that it follows, under the assumptions proposed by Werndl and Frigg, from standard arguments in probability theory.

Recent philosophy of measurement has emphasized the existence of both diachronic and synchronic “loops,” or feedback processes, in the epistemic achievements of measurement. A widespread response has been to conclude that measurement outcomes do not convey interest-independent facts about the world and that only a coherentist epistemology of measurement is viable. In contrast, I argue that a form of measurement realism is consistent with these results. The insight is that antecedent structure in measuring spaces constrains our empirical procedures such that successful measurement conveys a limited but veridical knowledge of “fixed points,” or modally stable, interest-independent features of the world.

This article considers the following question: What is the relationship between supervenience and reduction? I investigate this formally: first, by introducing a recent argument by Christian List to the effect that one can have supervenience without reduction; then, by considering how the notion of Nagelian reduction can be related to the formal apparatus of definability and translation theory; then, by showing how, in the context of propositional theories, topological constraints on supervenience serve to enforce reducibility; and, finally, by showing how constraints derived from the theory of ultraproducts can enforce reducibility in the context of first-order theories.

This article argues against Nina Emery’s view that there are compelling reasons to believe in nomological probabilities. To the degree that we can see no other way to detect the occurrence of the explanans apart from the bare fact that the explanandum happened, we may be skeptical that the proposed explanation is correct and open to the possibility that there is none. This provides us with a way to distinguish physical entities from mathematical ones, illuminating interpretations of quantum mechanics.

In recent years, several authors have called to ground descriptive and normative decision theory on neuropsychological measures of utility. In this article, I combine insights from the best available neuropsychological findings, leading philosophical conceptions of welfare, and contemporary decision theory to rebut these prominent calls. I argue for two claims of general interest to philosophers, choice modelers and policy makers. First, severe conceptual, epistemic, and evidential problems plague ongoing attempts to develop accurate and reliable neuropsychological measures of utility. Second, even if these problems are solved, neuropsychological measures of utility lack the potential to directly inform welfare analyses and policy evaluations.