In Chapter 4 of Science and Subjectivity, I offered several arguments critical of Professor Thomas Kuhn's views as expressed in his influential book The Structure of Scientific Revolutions. His recent replies to these criticisms seem to me so inadequate as to suggest that he, and therefore others as well, may have failed to grasp their full import. Accordingly, I shall, in the first part of this paper, briefly recapitulate my earlier arguments and offer a short rejoinder to Professor Kuhn's replies. The second part of the paper will expand upon my earlier discussion to consider the basic metaphors of vision and revolution, offered by Kuhn to replace the traditional notion of deliberation. My argument here will be that these new metaphors are incongruous in critical respects, and my discussion will conclude by considering their relations to the contrast between understanding and accepting a theory.

Leitgeb and Pettigrew argue that (1) agents should minimize the expected inaccuracy of their beliefs and (2) inaccuracy should be measured via the Brier score. They show that in certain diachronic cases, these claims require an alternative to Jeffrey Conditionalization. I claim that this alternative is an irrational updating procedure and that the Brier score, and quadratic scoring rules generally, should be rejected as legitimate measures of inaccuracy.

Multifunctionality poses significant challenges for human brain mapping. Cathy Price and Karl Friston argue that brain regions perform many functions in one sense and a single function in another. Thus, neuroscientists must revise their “cognitive ontologies” to obtain systematic mappings. Colin Klein draws a different lesson from these findings: neuroscientists should abandon systematic mappings for context-sensitive ones. I claim that neither account succeeds as a general treatment of multifunctionality. I argue that brain areas, like genes or organs, are multifunctional in different ways. I call this the “functional heterogeneity hypothesis.” I contend that different multifunctional parts require different mapping strategies.

The paper defends the doctrine that Linnaean taxa, including species, have essences that are, at least partly, underlying intrinsic, mostly genetic, properties. The consensus among philosophers of biology is that such essentialism is deeply wrong, indeed incompatible with Darwinism. I argue that biological generalizations about the morphology, physiology, and behavior of species require structural explanations that must advert to these essential properties. The paper concludes by summarizing my responses to the objection that, according to current “species concepts,” species are relational, and to the objection that essentialism cannot accommodate features of Darwinism associated with variation and change.

Some philosophers of science—myself included—appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction making is essentially nontruth regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the essay is to describe these two packages. I comment also on how to choose between them.

Many have suggested that the transformation standardly referred to as ‘time reversal’ in quantum theory is not deserving of the name. I argue on the contrary that the standard definition is perfectly appropriate and is indeed forced by basic considerations about the nature of time in the quantum formalism.

Much contemporary epistemology is informed by a kind of confirmational holism, and a consequent rejection of the assumption that all confirmation rests on experiential certainties. Another prominent theme is that belief comes in degrees, and that rationality requires apportioning one's degrees of belief reasonably. Bayesian confirmation models based on Jeffrey Conditionalization attempt to bring together these two appealing strands. I argue, however, that these models cannot account for a certain aspect of confirmation that would be accounted for in any adequate holistic confirmation theory. I then survey the prospects for constructing a formal epistemology that better accommodates holistic insights.

For nearly as long as the word ‘correlation’ has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence accumulated from non-experimental (observational) studies can be used to infer a causal relationship. In this paper, I compare the typical conditions required to infer that one variable is a direct cause of another in observational and experimental studies. I argue that they are essentially the same.

Darwin proposed that evolutionary novelties are environmentally induced in organisms “constitutionally” sensitive to environmental change, with selection effective owing to the inheritance of constitutional responses. A molecular theory of inheritance, pangenesis, explained the cross-generational transmission of environmentally induced traits, as required for evolution by natural selection. The twentieth-century evolutionary synthesis featured mutation as the source of novelty, neglecting the role of environmental induction. But current knowledge of environmentally sensitive gene expression, combined with the idea of genetic accommodation of mutationally and environmentally induced change, supports a revival of Darwin's original theory that is consistent with modern molecular and population genetics.

This paper distinguishes three concepts of “race”: bio-genomic cluster/race, biological race, and social race. We map out realism, antirealism, and conventionalism about each of these, in three important historical episodes: Frank Livingstone and Theodosius Dobzhansky in 1962, A. W. F. Edwards’s 2003 response to Lewontin’s 1972 paper, and contemporary discourse. Semantics is especially crucial to the first episode, while normativity is central to the second. Upon inspection, each episode also reveals a variety of commitments to the metaphysics of race. We conclude by interrogating the relevance of these scientific discussions for political positions and a post-racial future.

I suggest following Paul Feyerabend's own advice, and interpreting Feyerabend's work in light of the principles laid out by John Stuart Mill. A review of Mill's essay, On Liberty, emphasizes the importance Mill placed on open and critical discussion for the vitality and progress of various aspects of human life, including the pursuit of scientific knowledge. Many of Feyerabend's more unusual stances, I suggest, are best interpreted as attempts to play certain roles—especially the role of “defender of unpopular minority opinion”—that are necessary to fulfilling Mill's conditions for rational exchange and optimal human development.

A common methodological adage holds that diverse evidence better confirms a hypothesis than does the same amount of similar evidence. Proponents of Bayesian approaches to scientific reasoning such as Horwich, Howson and Urbach, and Earman claim to offer both a precise rendering of this maxim in probabilistic terms and an explanation of why the maxim should be part of the methodological canon of good science. This paper contends that these claims are mistaken and that, at best, Bayesian accounts of diverse evidence are crucially incomplete. This failure should lend renewed force to a long-neglected global worry about Bayesian approaches.

Why does classical equilibrium statistical mechanics work? Malament and Zabell (1980) noticed that, for ergodic dynamical systems, the unique absolutely continuous invariant probability measure is the microcanonical. Earman and Rédei (1996) replied that systems of interest are very probably not ergodic, so that absolutely continuous invariant probability measures very distant from the microcanonical exist. In response I define the generalized properties of epsilon-ergodicity and epsilon-continuity, I review computational evidence indicating that systems of interest are epsilon-ergodic, I adapt Malament and Zabell's defense of absolute continuity to support epsilon-continuity, and I prove that, for epsilon-ergodic systems, every epsilon-continuous invariant probability measure is very close to the microcanonical.

Bayesian, Jeffrey and Field conditionals are compared and it is shown why the last two cannot be reduced to the first. Maximum relative entropy is used in two kinds of justification of the Field conditional and the dispensability of entropy principles in general is discussed.

A survey of the literature on the problem of the synthetic a priori soon reveals that the term “analytic” is used in a narrower and a broader sense. In the narrower sense, a proposition is analytic if it is either a truth of logic or is logically true. By saying of a proposition that it is logically true, I mean, roughly, and with an eye on the problem of the relation of logical categories to natural languages, that when defined terms are replaced by their definientia, it becomes a substitution instance of a truth of logic. And a truth of logic can be adequately characterized for present purposes as a proposition which occurs in the body of Principia Mathematica, or which would properly occur in a vermehrte und verbesserte Auflage of this already monumental work. If we now agree to extend the convenient phrase “logically true” to cover truths of logic as well as propositions which are logically true in the sense just defined, we can say that an analytic proposition in the narrower sense is a proposition which is logically true.

The syllogism has often been criticized. Yet the theory of the syllogism cannot be omitted from logic. Even if it were not for its historical significance, its nature as a chapter of class logic assigns to it a place in any presentation of logic.

The usual exposition of the theory of the syllogism, however, whether given by the use of the familiar rules of the syllogism, or by the help of diagrams, appears clumsy and lacks the lucidity of modern chapters of logic. The reason seems to be given in the inefficient notation, taken over from ancient and medieval logic. In the following I should like to present an improved notation, which combines some of the traditional features with modern ones, and which is based on the criticism of the syllogistic theory which I have given elsewhere. It will be seen that in the revised form the theory of the syllogism is apt to meet the standards of modern logic.

This paper is an empirical critique of causal accounts of scientific explanation. Drawing on explanations which rely on nonlinear dynamical modeling, I argue that the requirement of causal relevance is both too strong and too weak to be constitutive of scientific explanation. In addition, causal accounts obscure how the process of mathematical modeling produces explanatory information. I advance three arguments for the inadequacy of causal accounts. First, I argue that explanatorily relevant information is not always information about causes, even in cases where the explanandum has an identifiable causal history. Second, I argue that treating theoretical explanations as reductions from general causal laws does not accurately describe the types of “top-down” explanations typical of dynamical modeling. Finally, I argue that causal/mechanical accounts of explanation are intrinsically vulnerable to the irrelevance problem.

Game theoretic explanations of the evolution of human behavior have become increasingly widespread. At their best, they allow us to abstract from misleading particulars in order to better recognize and appreciate broad patterns in the phenomena of human social life. We discuss this explanatory strategy, contrasting it with the particularist methodology of contemporary evolutionary psychology. We introduce some guidelines for the assessment of evolutionary game theoretic explanations of human behavior: such explanations should be representative, robust, and flexible. Distinguishing these features sharply can help to clarify the import and accuracy of game theorists' claims about the robustness and stability of their explanatory schemes. Our central example is the work of Brian Skyrms, who offers a game theoretic account of the evolution of our sense of justice. Modeling the same Nash game as Skyrms, we show that, while Skyrms' account is robust with respect to certain kinds of variation, it fares less well in other respects.