Itamar Pitowsky's book (1989), published in the Springer-Verlag Lecture Notes in Physics series, brings together several extremely interesting component investigations concerning the foundations of quantum mechanics. All deal with issues of probability including, in one case, the relation of probability to logic. It is a significant contribution, offering both new, nontrivial mathematical results, and provocative philosophical remarks about their significance.

Popper's paradox of ideal evidence has long been viewed as a telling criticism of Keynes's logical theory of probability and its associated concept of the weight of argument. This paper shows that a simple addition to Keynes's definitions of irrelevance enables his theory to elude the paradox with ease. The modified definition draws on ideas already present in Keynes's Treatise on Probability (1973). As a consequence, relevant evidence and the weight of argument may increase, even when new evidence leaves the probability unaltered.

Statistical decision theory, whether based on Bayesian principles or other concepts such as minimax or admissibility, relies on minimizing expected loss or maximizing expected utility. Loss and utility functions are generally treated as unit-less numerical measures of value for consequences. Here, we address the issue of the units in which loss and utility are settled and the implications that those units have on the rankings of potential decisions. When multiple currencies are available for paying the loss, one must take explicit account of which currency is used as well as the exchange rates between the various available currencies.

The realism/antirealism controversy has gone on for centuries, and gives every indication that it will continue to go on for centuries. Dismayed, I take a closer look at it. I find that the question it poses—very roughly, whether scientific knowledge is true (approximately true, put forward as true, etc.) or only useful (empirically adequate, a convenient method of representation, etc.)—actually suppresses socially critical thought and discussion about science (e.g., concerning whether scientific knowledge is sexist or racist or socially harmful in other ways, or whether scientific knowledge is useful for achieving the goals we have or the goals we ought to have). I find, as well, that two of the most important responses to the realism/antirealism controversy—that which construes it as concerned with science's aims and that which construes it as concerned with science's results—fail to make sufficient empirical or normative contact with science. As a consequence, they provide representations of science that either do not help scientists (or nonscientists) make informed decisions about science, or actually hinder these individuals from doing so. I conclude that we should either stop engaging in the realism/antirealism controversy entirely, or else engage in it in a more socially responsible way—by gathering the right kinds of empirical and normative data, and framing more helpful versions of the questions we want to ask. I end by responding to the strong objections sure to be voiced by my colleagues in philosophy of science.

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts.

Theoretical accounts of health attempt to ground the concept in the relevant underlying biological facts. Discussions of such accounts have largely focused on whether they successfully identify necessary and sufficient conditions for a state to count as pathological. Correctly accounting for examples of pathology, however, is not the only basis for evaluating an understanding of disease. Here I argue that we should expect any understanding of health and disease to be consistent with the view that medicine’s central aim is health promotion. I argue that the theoretical account of health offered by Christopher Boorse faces particular difficulties in this regard.

For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size (n) to learn from data by securing (i)–(ii). It is argued that the criticism of the postdata severity evaluation of testing results based on a small n by Rochefort-Maranda (2020) is meritless because it conflates [a] misuses of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature.

Practice-centric and theory-centric approaches in philosophy of science are described and contrasted. The contrast is developed through an examination of their different treatments of the underdetermination problem. The practice-centric approach is illustrated by a summary of comparative research on approaches in the biology of behavior. The practice-centric approach is defended against charges that it encourages skepticism regarding the sciences.

Recently, Rueger and Sharp (1996) and Koperski (1998) have been concerned to show that certain procedural accounts of model confirmation are compromised by non-linear dynamics. We suggest that the issues raised are better approached by considering whether chaotic data analysis methods allow for reliable inference from data. We provide a framework and an example of this approach.

Jonah N. Schupbach and Jan Sprenger have proposed conditions of adequacy for measures of explanatory power. They derive and defend a measure of explanatory power satisfying their conditions of adequacy. This article furthers the development of their measure. The requirement that the measure be multidimensional analytic (in the sense of the mathematics of several complex variables) is avoided. Several proofs are simplified, and gaps in proofs are filled.

Will a synthesis of developmental and evolutionary biology require a focus on the role of nongenetic resources in evolution? Nongenetic variation may exist but be hidden because the phenotypes are stable (developmentally canalized) under certain background conditions. In this case, those differences may come to play important roles in evolution when background conditions change. If this is so, then a focus on the way that developmental resources are made reliable, and the ways in which reliability fails, may prove to be of crucial importance to linking developmental and evolutionary biology.

In (1981), Levi has laid out the issues involving chances, frequencies, and direct inference with admirable precision. Nevertheless, puzzles remain. The chief puzzle to which I wish to draw attention is this: Under certain circumstances, we can combine knowledge of chances and knowledge of frequencies to yield new knowledge of chances. If Petersen is “drawn at random” from among Swedes, and we also know that the proportion of Protestants among Swedes is 0.9, then we can say that the chance that Petersen is a Protestant is 0.9. But if we apply this principle generally, we are led to generally trivial results: direct inference yields probabilities constrained only to lie in a broad interval like [0, 1]. On the other hand, if we can't always combine knowledge of chances and knowledge of frequencies to get new knowledge of chances, or if this knowledge can be overridden by other considerations, how do we know when we can usefully apply direct inference?

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of error, a potential path that is already being explored by some cosmologists.

Scientists are becoming increasingly aware of a “replicability crisis” in the behavioral, social, and biomedical sciences. Researchers have made progress identifying statistical and methodological causes of the crisis. However, the social structure of science is also to blame. In the fields affected by the crisis, nobody is explicitly responsible and rewarded for doing confirmation and replication work. This article makes the case for a social structural reform to address the problem. I argue that we need to establish a reward system that supports a dedicated group of confirmation researchers and formulate a proposal that would achieve this.

In this paper, I will defend Paul Feyerabend's claim—that there are some scientific theories that cannot be refuted unless one of their rivals is first confirmed—by criticizing Ronald Laymon's well-known attack on Feyerabend's claim. In particular, I will argue both that the Second Law of Thermodynamics was not refuted before the Kinetic Theory's predictions were confirmed, and that it could not have been refuted without the confirmation of the remarkable predictions of some rival theory.

It is an interesting paradox that on the one hand intemperate enthusiasm greets new medical discoveries. On the other hand, the lack of science in medicine is paraded from time to time, usually as a matter of apologetics, as, when a physician wishes to excuse an error, a lawyer to discredit a physician, or a jury to render a verdict contrary to medical evidence. Philosophers who insist on the mathematical or quantitative aspects in any definition of science ascribe very little that is truly scientific to biology in general or to its daughter medicine in particular. Jerome Frank comments approvingly that “many scientific-minded physicians today deny that medicine is, or is likely ever to be, a science.” (4) Articulate physicians themselves may be highly critical. Dr. Ian Stevenson, for example, declares, “Medicine will not achieve the status of a science until the basic laws of health and disease have been disclosed. But the search for these laws has hardly begun.” (10) Stevenson goes on to point out that instead of seeking basic laws, medicine is concentrating on facts, from which no laws emerge.

Analysis of two key ways of characterizing genes—as causes of phenotypic effects and as genomic DNA sequences—has yielded widespread pessimism that they can be united in a coherent gene concept. This raises important questions about the epistemology of genomic research: If analysis of a genome sequence cannot yield information about genes defined both in terms of their products and their DNA sequence, then what could we learn from it? I investigate basic tools of genomic analysis, argue that they do not reflect the application of either gene concept, and clarify how we learn from genomic research.