Work

"The Logit Model Measurement Problem" (2025) Philosophy of Science 92 (2):285-303

Traditional wisdom dictates that statistical model outputs are estimates, not measurements. Despite this, statistical models are employed as measurement instruments in the social sciences. In this article, I scrutinize the use of a specific model—the logit model—for psychological measurement. Given the adoption of a criterion for measurement that I call comparability, I show that the logit model fails to yield measurements due to properties that follow from its fixed residual variance.

"Language dependence and objectivity in inductive logic" (2026) Synthese 208 (197)

In this paper I offer a new defense of Carnap's inductive logic and the logical interpretation of probability that goes with it. While many consider language relativity to undermine the objectivity of Carnap's system of inductive logic, I show that deductive logic is vulnerable to the same concerns about language relativity. It is better, I propose, to accept that both inductive and deductive logic offer an account of objective relations between propositions but not objective initial truth value or probability assignments. I argue that the logical interpretation of probability is the correct interpretation of non-physical probability, where non-physical probability characterizes, for example, the non-deductive support relation between evidence and hypothesis.

"Carnap and Bar-Hillel's Theory of Semantic Information" (2026) Semantic Information: Formal Perspectives. In Carnap-Handbuch: Leben – Werk – Wirkung. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 357-362

"Reliabilism and Hypothesis Testing" [unpublished draft here]

Frequentist hypothesis testers in statistics face the probabilist objection: according to the probabilist objection, frequentist inferences about hypotheses are not justified because the probability of the hypothesis is not given. Frequentist hypothesis testing uses the probability of the evidence given a hypothesis under consideration in order to make inferences (the p-value). If the evidence is very unlikely given the hypothesis, the hypothesis is rejected. This pattern of inference is invalid, the probabilist objector complains, because one needs the probability of the hypothesis under consideration given the evidence to make an inference about the hypothesis. In other words, the frequentist hypothesis tester commits the base-rate fallacy. In this paper, I show that an attempt to avoid the probabilist objection by appeal to reliabilism fails.

A paper on machine learning (email for manuscript)

Models in machine learning are opaque - some call them 'black-box models' for this reason. It is often argued that opaque models need special ethical and legal attention not afforded to classical statistical models. There is no consensus in the literature, however, about what opacity is and how it distinguishes models in machine learning from classical statistical models. Words like 'explainability' and 'interpretability' are used interchangeably to characterize model opacity.

In this paper, I argue for three main conclusions: (1) If we recognize a distinction between formal and efficient explanations, then black-box models are explainable, at least formally. (2) Given that black-box models are formally explainable, opacity is best understood as a lack of interpretability rather than a lack of explainability. Interpretability, I argue, can be precisely defined as a mapping between model terms and relations in a real system. (3) Once model opacity is defined in terms of interpretability rather than explainability, some legal and ethical worries about the use of black-box models for public-facing decisions are deflated.

A paper on the paradox of voting (email for manuscript)

Voting faces a paradox of collective action. Each individual vote is nearly guaranteed to make no difference in the outcome of an election. Submitting a vote has a cost, so the expected utility of voting is less than 0. It is rational, then, to abstain from voting \citep{downs1957economic}. But \textit{somebody} has to vote for democracy to work.

The paradox of voting is cleanly demonstrated using causal decision theory. The act of voting is likely to be causally inert given the problem's setup, and so it is not rational to do it given the cost. Some author's have suggested that adopting evidential decision theory can offer a solution to the paradox of voting by explaining why, on EDT, it is rational to vote \citep{ahmed2014evidence,grafstein1991evidential,grafstein2018everyones}. The paradox of voting turns out to be a Newcomb problem, too: a decision problem that calls into question the use of causally inert associations for decision-making.

In this paper, I flesh out an evidential decision theoretic solution to the paradox of voting. Such a solution has been suggested by Grafstein and Ahmed, though not developed in detail as it is here. Then, I argue that the EDT solution fails. Unsurprisingly, it fails if one denies (as causal decision theorists do) that it is rational to act on the basis of merely evidential probabilities. More significantly, though, it fails due to internal inconsistency: on EDT there are no constraints indicating how to partition the possibility space such that the categories are relevant to inference.