We identify a species of experiment—Kon-Tiki experiments—used to demonstrate the competence of a cause to produce a certain effect, and we examine their role in the historical sciences. We argue that Kon-Tiki experiments are used to test middle-range theory, to test assumptions within historical narratives, and to open new avenues of inquiry. We show how the results of Kon-Tiki experiments are involved in projective (rather than consequentialist) inferences, and we argue (against Kyle Stanford) that reliance on projective inferences does not provide historical scientists with any special protection against the problem of unconceived alternatives.

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.

I argue that diversity and pluralism are valuable not just for science but for philosophy of science. Given the partiality and perspectivism of representation, pluralism preserving integration can increase accuracy. Perspectivism is often supported by appeal to visual representation. I draw further insights from multimodel sensory integration for understanding experiment-based predictions of protein structure. The epistemic lessons learned from the scientific case also apply to philosophy of science itself. Finally, I suggest that a critical, nuanced philosophical view of legitimate sources of pluralism in science has an important role to play in public discourse.

We consider the nature of quantum randomness and how one might have empirical evidence for it. We will see why, depending on one’s computational resources, it may be impossible to determine whether a particular notion of randomness properly characterizes one’s empirical data. Indeed, we will see why even an ideal observer under ideal epistemic conditions may never have any empirical evidence whatsoever for believing that the results of one’s quantum-mechanical experiments are randomly determined. This illustrates a radical sort of empirical underdetermination faced by fundamentally stochastic theories like quantum mechanics.

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.

Generalized Darwinism models cultural development as an evolutionary process, where traits evolve through variation, selection, and inheritance. Inheritance describes either a discrete unit’s transmission or a mixing of traits (i.e., blending inheritance). In this article, we compare classical models of cultural evolution and generalized population dynamics with respect to blending inheritance. We identify problems of these models and introduce our model, which combines relevant features of both. Blending is implemented as success-based social learning, which can be shown to be an optimal strategy.

The “Cosmological Constant Problem” (CCP) is widely considered a crisis in contemporary theoretical physics. Unfortunately, the search for its resolution is hampered by open disagreement about what is, strictly, the problem. This disagreement stems from the observation that the CCP is not a problem within any of our current theories, and nearly all of the details of those future theories for which the CCP could be made a problem are up for grabs. Given this state of affairs, I discuss how one ought to make sense of the role of the CCP in physics and generalize some lessons from it.

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are data-laden and data are model-laden. In this article I elaborate and defend the second, more controversial, component of the symbiosis view and construct a taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than so-called raw data and, hence, beneficially serve the epistemic aims of science.

The article addresses the debate about the empirical status of particles versus wave functions in Bohmian quantum mechanics. It thereby clarifies questions and misconceptions about the role of the particles in the measurement process, the (un)reliability of position measurements (surrealistic trajectories), and the limited empirical access to particle positions (absolute uncertainty). Taking the ontological commitment of Bohmian mechanics seriously, all relevant empirical results follow from an analysis of the theory in terms of particle motions. Finally, we address the question of why particle motions rather than patterns in the wave function would be the supervenience base of conscious experience.

In 2012, the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth’s history, had one-quarter of its boundaries moved in a widespread revision of radiometric dates. The philosophy of metrology helps us understand this episode, and it, in turn, elucidates the notions of calibration, coherence, and consilience. I argue that coherence testing is a distinct activity preceding calibration and consilience, and I highlight the value of discordant evidence and trade-offs scientists face in calibration. The iterative nature of calibration, moreover, raises the problem of legacy data.

I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis refers to a beneficial, two-way interdependence between a particular model and data set or between closely related models and data sets. For each example presented here, I show how the symbiotic relationship works—what the interdependence consists in and how it is supposed to be beneficial—and I consider whether, alongside the benefits, there is some risk of problematic circularity.

In 1932, John von Neumann argued for the equivalence of the thermodynamic entropy and −Trρlnρ, since known as the von Neumann entropy. Meir Hemmo and Orly R. Shenker recently challenged this argument by pointing out an alleged discrepancy between the two entropies in the single-particle case, concluding that they must be distinct. In this article, their argument is shown to be problematic as it (a) allows for a violation of the second law of thermodynamics and (b) is based on an incorrect calculation of the von Neumann entropy.

The article investigates the relations between Hausdorff and non-Hausdorff manifolds as objects of general relativity. We show that every non-Hausdorff manifold can be seen as a result of gluing together some Hausdorff manifolds. In the light of this result, we investigate a modal interpretation of a non-Hausdorff differential manifold, according to which it represents a bundle of alternative space-times, all of which are compatible with a given initial data set.

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.

Many theories of actual causation implicitly endorse the claim that if c is an actual cause of e, then either c causes e directly or every intermediary by which c indirectly causes e is itself both an actual cause of e and also an actual effect of c. We think this compositionality constraint is plausible. However, as we show, it is not always satisfied by the causal attributions ordinary people make. We conclude by considering what philosophers working on causation should do when the deliverances of their theories diverge from what ordinary people say.

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.”

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessing a model’s adequacy-for-purpose differ from assessing its representational accuracy? In addition, responses are given to some objections that might be raised against an adequacy-for-purpose view.

The concept of emergence is commonly invoked in modern physics but rarely defined. Building on recent influential work by Jeremy Butterfield, I provide precise definitions of emergence concepts as they pertain to properties represented in models, applying them to some basic examples from space-time and thermostatistical physics. The chief formal innovation I employ, similarity structure, consists in a structured set of similarity relations among those models under analysis—and their properties—and is a generalization of topological structure. Although motivated from physics, this similarity-structure-based account of emergence applies to any science that represents its possibilia with (mathematical) models.

A number of philosophers draw a close analogy between scientific modeling and fiction, often appealing to Kendall Walton’s make-believe view. I assess the models-fictions analogy from a cognitive angle, suggesting that from this perspective it appears relatively weak. More specifically, I argue that, on the one hand, the appeal to Walton is appropriate inasmuch as his view fits well with how modelers employ the imagination. On the other hand, what makes Walton’s view attractive as an account of the cognitive aspects of modeling makes it less attractive as an account of fiction.