This article develops a new, general account of replication (the Resampling Account of replication). I argue that a replication is an experiment that resamples the experimental components of an original experiment that are treated as random factors and that the function of replications is, narrowly, to assess the reliability of the replicated experiments. On this basis, I argue that the common notion of conceptual replication is confused and that the ongoing controversy about the relative value of direct and conceptual replications should be dissolved.

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have suggested treating opaque systems instrumentally, but computer scientists developing strategies for increasing transparency are correct in finding this unsatisfying. Instead, I propose an analysis of transparency as having three forms: transparency of the algorithm, the realization of the algorithm in code, and the way that code is run on particular hardware and data. This targets the transparency most useful for a task, avoiding instrumentalism by providing partial transparency when full transparency is impossible.

There is a vast literature within philosophy of mind that focuses on artificial intelligence but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and targets are picked out, how modeling inferences are justified, and the metaphysical status of models.

I argue that it is possible to give an interpretation of the classical $\hslash \to 0$ limit of quantum mechanics that results in a partial explanation of the success of classical mechanics. The interpretation is novel in that it allows one to explain the success of the theoretical structure of classical mechanics. This interpretation clarifies the relationship between physical quantities and propositions in quantum theories and provides a precise notion of a quantum theory holding “approximately on certain scales.”

This article examines the multiple realizability thesis within a causal framework. The beginnings of this framework are found in Elliott Sober’s “Multiple Realizability Argument against Reduction,” which argues that the multiple realizability thesis poses no challenge to reductive explanation. While Sober’s causal approach has the potential to reveal new insights, I argue that his setup fails to capture important aspects of the multiple realizability thesis. After correcting for these issues, I argue that this causal framework reveals something quite different. It reveals how multiple realizability relates to a common type of causal complexity in biology that poses problems for reductive explanation.

Thomas Polger and Lawrence Shapiro claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs, which recur in different organisms and species and perform specific functions. I show that network motifs with entirely different underlying causal structures can perform the same function of interest. The article also considers the scope problem of multiple realization.

Computer scientists have provided a distinct justification of Occam’s Razor. Using the probably approximately correct framework, they provide a theorem that they claim demonstrates that we should favor simpler hypotheses. The argument relies on a philosophical interpretation of the theorem. I argue that the standard interpretation of the result in the literature is misguided and that a better reading does not, in fact, support Occam’s Razor at all. To this end, I state and prove a very similar theorem that, if interpreted the same way, would justify the contradictory Anti-Occam’s Razor—the principle that we should favor more complex hypotheses.

I propose a framework for pragmatic reliability in-the-limit criteria, extending the epistemic reliability framework. I identify some common scientific contexts that complicate the application or interpretation of epistemic reliability criteria, drawing heavily from economics for illustrative examples. I then propose an extension of the standard framework, where inquiry is constrained by both epistemic and nonepistemic factors. This provides analogous notions of pragmatic underdetermination and pragmatic reliability with respect to a particular goal, as well as a principled method for extracting solvable problems from unsolvable ones.

Comparativism is the view that comparative confidences (e.g., being more confident that P than that Q) are more fundamental than degrees of belief (e.g., believing that P with some strength x). I outline the basis for a new, nonprobabilistic version of comparativism inspired by a suggestion made by Frank Ramsey in “Probability and Partial Belief.” I show how, and to what extent, ‘Ramseyan comparativism’ might be used to weaken the (unrealistically strong) probabilistic coherence conditions that comparativism traditionally relies on.