Howson (2000) contends that the No-Miracles argument fails as an argument in support of scientific realism because it commits the base-rate fallacy. Psillos (2009) has defended the argument by appealing to cases that involve conditional probabilities but where base-rate information can properly be ignored. I show that Psillos’s defense of the No-Miracles argument is inadequate and that the prospects for a purely probabilistic formulation of the argument are dim. I end by considering whether interpreting the argument as an inference to the best explanation might better serve the scientific realist, concluding that such an approach would limit the realist’s ability to effectively counter the Pessimistic Induction.

I propose an account of the model-based structure of the present-day high energy physics experiments in which the relations among the theoretical, experimental and simulation models constitute a non-linear structure that is akin to a network of models (NoM). I argue that while the proposed NoM subsumes Suppes’ hierarchy of models (HoM) as the model-based characterization of the inference leading from the data to the validity or invalidity of the hypothesis tested in an experiment, it involves a model-based characterization of the inference leading from the collision of particles to the acquisition of data, which is missing in Suppes’ HoM.

Physics presents us with a symphony of natural constants: $G$, ℏ, $c$, and so forth. Up to this point, constants have received comparatively little philosophical attention. In this article, I provide an account of dimensionful constants, in particular the gravitational constant. I propose that they represent interquantity structure in the form of relations between quantities with different dimensions. I use this account of $G$ to settle a debate over whether mass scalings are symmetries of Newtonian gravitation. I argue that they are not, but only if we interpret mass anti-quidditistically. This is analogous to anti-haecceitism in the presence of spacetime symmetries.

Judging a person as morally responsible involves believing that certain responses (such as punishment, reward, or expressions of blame or praise) can be justifiably directed at the person. This paper develops an account of the evolution of moral responsibility judgment that adopts Michael Tomasello’s two-step theory of the evolution of morality and borrows also from Christopher Boehm’s work. The main hypothesis defended is that moral responsibility judgment originally evolved as an adaptation that enabled groups of cooperative individuals to hold free riders responsible more safely by acting in a coordinated way.

Economists often consult multiple models in order to combat model uncertainty in the face of misspecification. By examining modeling practices at the Bank of England, this paper identifies an important, but underappreciated modeling procedure. Sometimes an idealized model is manipulated to reproduce the results from another distinct auxiliary model, ones which it could not produce on its own. However, this procedure does not involve making the original model “more realistic,” insofar as this means adding in additional causal factors. This suggests that there are ways to make models more representationally adequate that do not involve de-idealization in the straightforward sense.

I argue that the temporal asymmetry of influence is not merely the result of thermodynamics: it is a consequence of the fact that the modal structure of the universe must admit only processes that cannot give rise to contradictions. I appeal to the process matrix formalism developed in the field of quantum foundations to characterize processes that are compatible with local free will while ruling out contradictions, and I argue that this gives rise to “consistent-chaining” requirements that explain the temporal asymmetry of influence. I compare this view to the perspectival account of causation advocated by Price and Ramsey.

The COVID-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modelers. This raises novel questions for the philosophy of science. Here I investigate the possibility of 'natural models' in predicting the SARS-Cov-2 virus’s trajectory for epidemiological purposes. I argue that to the extent that these can be considered scientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, I draw from work on both model organisms and natural experiments as well as recent work in epidemiology.

This paper concerns the recent revival of entity realism. Having been started with the work of Ian Hacking, Nancy Cartwright, and Ronald Giere, the project of entity realism has recently been developed by Matthias Egg, Markus Eronen, and Bence Nanay. The paper opens a dialogue among these recent views on entity realism and integrates them into a more advanced view. The result is an epistemological criterion for reality: the property-tokens of a certain type may be taken as real insofar as only they can be materially inferred from the evidence obtained in a variety of independent ways of detection.

I consider a problem for functional reductionism based on the following tension. Say that b is functionally reduced to a. On one hand, a and b turn out to be identical, and identity is a symmetric relation. On the other hand, functional reductionism implies that a and b are asymmetrically related: if b is functionally reduced to a, then a is not functionally reduced to b. Thus we ask, how can a and b be asymmetrically related if they are the same thing? I propose a solution to this tension by distinguishing between ontological levels and levels of description.

Scientific disagreements are an important catalyst for scientific progress. But what happens when scientists disagree amid times of crisis, when we need quick yet reliable policy guidance? In this article, we provide a normative account for how scientists facing disagreement in the context of “fast science” should respond and how policy makers should evaluate such disagreement. Starting from an argumentative, pragma-dialectic account of scientific controversies, we argue for the importance of higher-order evidence (HOE), and we specify desiderata for scientifically relevant HOE. We use our account to analyze the controversy about the aerosol transmission of COVID-19.

I present new counterexamples to the asymmetry of grounding: we have prima facie reason to think that some conditional probabilities partially ground their inverse conditional probabilities, and vice versa. These new counterexamples may require that we reject the asymmetry of grounding or, alternatively, that we reject one or more of the assumptions (regarding, e.g., the correct interpretation of probability) that enable the counterexamples. Either way, by reflecting on these purported counterexamples to grounding asymmetry, we learn something important, either about the formal properties of grounding or about the nature of probability (or both).

Piccinini’s usability constraint states that physical processes must have “physically constructible manifestation[s]” to be included in epistemically useful models of physical computation. But to determine what physical processes can be implemented in physical systems (as parts of computations), we must already know what physical processes can be implemented in physical systems (as parts of processes for constructing computing systems). We need additional assumptions about what qualifies as a building process. Piccinini implicitly assumes a classical computational understanding of executable processes, but this is an assumption imposed on physical theories and may artificially limit our picture of epistemically useful physical computation.

Gerhard Schurz claims to have a solution to Hume’s problem of induction based on results from machine learning concerning meta-induction. His argument has two steps. The first is to establish a justification for following a certain meta-inductive strategy based on its predictive optimality. The second step is to show how this justification can be transferred to object-induction. I unpack the second step and fail to find a convincing argument supporting the transfer of justification from meta-induction to object-induction. My conclusion is that the problem of induction has not yet been solved by appeal to meta-induction.

In “The Role of Historical Science in Methodological Actualism,” Meghan Page makes a number of important points about the sciences of geohistory. Yet the force of her arguments is blunted by her decision to explicate concepts like “actualism” and “uniformitarianism” as abstract methodological commitments. In this discussion note, I illustrate the difficulties that arise from this decision, and suggest a way of avoiding them that preserves Page’s main conclusions.

In a recent article on the interpretation of probability in evidence-based medical practice, Jonathan Fuller argues that we should interpret probabilities as credences in individual cases because this avoids some important problems. In this article, I argue that Fuller misidentifies the real issue and so fails to offer a meaningful solution to it. The real problem with making probability judgments in individual cases is deciding which objective considerations ought to constrain our formation of credences. This leads us to the reference class problem, which, as Alan Hajek argues, is a problem for any interpretation of probability.