The renormalization group (RG) has been characterized as merely a coarse-graining procedure that does not illuminate the microscopic content of quantum field theory (QFT) but merely gets us from that content, as given by axiomatic QFT, to macroscopic predictions. I argue that in the constructive field theory tradition, RG techniques do illuminate the microscopic dynamics of a QFT, which are not automatically given by axiomatic QFT. RG techniques in constructive field theory are also rigorous, so one cannot object to their foundational import on grounds of lack of rigor.

Boltzmannian statistical mechanics partitions the phase space of a system into macroregions, and the largest of these is identified with equilibrium. What justifies this identification? Common answers focus on Boltzmann’s combinatorial argument, the Maxwell-Boltzmann distribution, and maximum entropy considerations. We argue that they fail and present a new answer. We characterize equilibrium as the macrostate in which a system spends most of its time and prove a new theorem establishing that equilibrium thus defined corresponds to the largest macroregion. Our derivation is completely general and does not rely on assumptions about the dynamics or interparticle interactions.

A dynamical system is called chaotic if small changes to its initial conditions can create large changes in its behavior. By analogy, we call a dynamical system structurally chaotic if small changes to the equations describing the evolution of the system produce large changes in its behavior. Although there are many definitions of “chaos,” there are few mathematically precise candidate definitions of “structural chaos.” I propose a definition, and I explain two new theorems that show that a set of models is structurally chaotic if it contains a chaotic function. I conclude by discussing the relationship between structural chaos and structural stability.

Focusing on Shannon information, this article shows that, even on the basis of the same formalism, there may be different interpretations of the concept of information, and that disagreements may be deep enough to lead to very different conclusions about the informational characterization of certain physical situations. On this basis, a pluralist view is argued for, according to which the concept of information is primarily a formal concept that can adopt different interpretations that are not mutually exclusive, but each useful in a different specific context.

Election prediction by means of opinion polling is a rare empirical success story for social science. I examine the details of a prominent case, drawing two lessons of more general interest: (1) Methodology over metaphysics. Traditional metaphysical criteria were not a useful guide to whether successful prediction would be possible; instead, the crucial thing was selecting an effective methodology. (2) Which methodology? Success required sophisticated use of case-specific evidence from opinion polling. The pursuit of explanations via general theory or causal mechanisms, by contrast, turned out to be precisely the wrong path—contrary to much recent philosophy of social science.

To arrive at their final evaluation of a manuscript or grant proposal, reviewers must convert a submission’s strengths and weaknesses for heterogeneous peer review criteria into a single metric of quality or merit. I identify this process of commensuration as the locus for a new kind of peer review bias. Commensuration bias illuminates how the systematic prioritization of some peer review criteria over others permits and facilitates problematic patterns of publication and funding in science. Commensuration bias also foregrounds a range of structural strategies for realigning peer review practices and institutions with the aims of science.

On many science-related policy questions, the public is unable to make informed decisions, because of its inability to make use of knowledge obtained by scientists. Philip Kitcher and James Fishkin have both suggested therefore that on certain science-related issues, public policy should not be decided on by actual democratic vote, but should instead conform to the public’s counterfactual informed democratic decision (CIDD). Indeed, this suggestion underlies Kitcher’s specification of an ideal of a well-ordered science. This article argues that this suggestion misconstrues the normative significance of CIDDs. At most, CIDDs might have epistemic significance, but no authority or legitimizing force.

In this article I analyze process tracing, a causal mechanism-based technique for testing causal claims in the social sciences that requires one to specify a chain of intervening causes between any putative cause and effect. I argue that one should not only give evidence that the intervening causes are present in a suitable case study, as process tracing methodologists recommend, but also provide counterfactual evidence to show that each link in the chain is genuinely causal. I detail what that counterfactual evidence should consist of, using Woodward’s manipulability theory, and argue that this evidence relies on tentative comparisons to other case studies.

I first propose to distinguish between two kinds of external validity inferences, predictive and explanatory. I then argue that we have a satisfactory answer to the question of the conditions under which predictive external validity inferences are good. If this claim is correct, then it has two immediate consequences: First, some external validity inferences are deductive, contrary to what is commonly assumed. Second, Steel’s requirement that an account of external validity inference break what he calls the ‘Extrapolator’s Circle’ is misplaced, at least when it comes to predictive external validity inferences.

John Worrall and Nancy Cartwright have both argued that randomized controlled trials (RCTs) are “testing the wrong theory.” They claim that RCTs are designed to test inferences about the causal relationships in the study population, but this does not guarantee a justified inference about the causal relationships in the more diverse population in clinical practice. In this article I argue that the epistemology of theory testing in trials is more complicated than either Worrall’s or Cartwright’s accounts suggest. I illustrate this more complex theoretical structure with case studies in medical theory testing from Alzheimer’s research and anticancer drugs in personalized medicine.

The standard philosophical definition of placebos offered by Grünbaum is incompatible with Cartwright’s conception of randomized clinical trials. I offer a modified account of placebos that respects this role and clarifies why many current medical trials fail to warrant the conclusions they are typically seen as yielding. I then consider recent changes to guidelines for reporting medical trials and show that pessimism over parsing out the cause of “unblinding” is premature. Specifically, using a trial of antidepressants, I show how more sophisticated statistical analyses can parse out the source of such effects and serve as an alternative to placebo control.