This essay develops an account of health, the functional efficiency theory, which derives from Christopher Boorse's biostatistical theory (BST). Like the BST, the functional efficiency theory is a nonevaluative view of health, but unlike the BST, it argues that the fundamental theoretical task is to distinguish levels of efficiency with which the parts and processes within organisms and within systems within organisms function. Which of these to label as healthy or pathological is of secondary importance. Because the statistical distributions that Boorse's account relies on are distributions of functional efficiency, the functional efficiency theory arguably complements rather than challenges the BST.

Cooperation and the allocation of common resources are core features of social behavior. Games idealizing both interactions have been studied separately. But here, rather than examining the dynamics of the individual games, the interactions are combined so that players first choose whether to cooperate, and then, if they jointly cooperate, they bargain over the fruits of their cooperation. It is shown that the dynamics of the combined game cannot simply be reduced to the dynamics of the individual games and that both cooperation and fair division are more likely in the combined game than in the constituent games taken separately.

On the classical account of evolution by natural selection (ENS) found in Lewontin and many subsequent authors, ENS is conceived as involving three key ingredients: phenotypic variation, fitness differences, and heredity. Through the analysis of three problem cases involving heredity, I argue that the classical conception is substantially flawed, showing that heredity is not required for selection. I consider further problems with the classical account of ENS arising from conflations between three distinct senses of the central concept of ‘fitness’ and offer an alternative to the classical conception of ENS involving the interaction of distinct evolutionary mechanisms.

Characterizing the genetic structure of populations is of importance to evolutionary biology, to human disease gene mapping, and to forensic science. Sewall Wright introduced a set of F-statistics to describe population structure in 1951, and he emphasized that these quantities were ratios of variances. Responding to uncertainty over the best way to estimate F-statistics, Weir and Cockerham published a method-of-moments set of estimators in 1984. This paper continues to be widely cited, with over 7,000 citations to date. Some background to the publishing history of the Weir and Cockerham paper is given here, along with subsequent developments and a discussion of current uses of Wright's F-statistics.

Understanding the role mechanistic constraints play in shaping evolution can relieve the tension between the generally accepted intuition that there are no strict laws in biology and empirical findings showing that evolutionary processes are biased toward preferred outcomes. Mechanistic constraints explain why some evolutionary outcomes are more probable than others and allow for predictions in specific lineages. At the same time, mechanistic constraints are neither necessary nor universal in the way laws are traditionally characterized: they remain contingent on the past evolution of the biological mechanisms underpinning them and only constrain the future evolution of the organisms possessing them.

This article considers the Gibbs paradox and its implications for three definitions of entropy in statistical mechanics: (i) the “classical” Boltzmann entropy (SB1); (ii) the modified Boltzmann entropy that is usually proposed in response to the paradox (SB2); and (iii) a generalized version of the latter (SB2G). It is argued that (i) notwithstanding a recent suggestion to the contrary, the paradox does imply that SB1 is not a satisfactory definition of entropy; (ii) SB2 is undermined by “second-order” versions of the paradox; and (iii) SB2G solves the paradox but is not a satisfactory definition of entropy.

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.

We offer an account of empathetic pain that preserves the distinctions among standard pain, contagious pain, empathetic pain, sympathy for pain, and standard pain ascription. Vicarious experiences of both contagious and empathetic pain resemble to some extent experiences of standard pain. But there are also crucial dissimilarities. As neuroscientific results show, standard pain involves a sensorimotor and an affective component. According to our account, contagious pain consists in imagining the former, whereas empathetic pain consists in imagining the latter. We further argue that awareness of another's standard pain is part of empathetic pain, but empathetic awareness of another's standard pain differs from believing that another is in standard pain.

This article unpacks a particular use of ‘cases’ within developmental biology, namely as a means of describing the typical or canonical patterns of phenomena. The article explores how certain cases have come to be established within the field and argues that although they were initially selected for reasons of convenience or ease of experimental manipulation, these cases come to serve as key reference points within the field because of the epistemological structures imposed on them by the scientists using them and, hence, become usable in a wider variety of circumstances including future theory development.

Historically, one of the most controversial aspects of Darwinian evolution has been the prominent role that randomness and random change play in it. Most biologists agree that mutations in DNA have random effects on fitness. However, fitness is a highly simplified scalar representation of an enormously complex phenotype. Challenges to Darwinian thinking have focused on such complex phenotypes. Whether mutations affect such complex phenotypes randomly is ill understood. Here I discuss three very different classes of well-studied molecular phenotypes in which mutations cause nonrandom changes, based on our current knowledge. What is more, this nonrandomness facilitates evolutionary adaptation. Thus, living beings may translate DNA change into nonrandom phenotypic change that facilitates Darwinian evolution.

The concept of levels of organization is prominent in science and central to a variety of debates in philosophy of science. Yet many difficulties plague the concept of universal and discrete hierarchical levels, and these undermine implications commonly ascribed to hierarchical organization. We suggest the concept of scale as a promising alternative. Investigating causal processes at different scales allows for a notion of quasi levels that avoids the difficulties inherent in the classic concept of levels. Our primary focus is ecology, but we suggest how the results generalize to other invocations of hierarchy in science and philosophy of science.

In an essay recently published in this journal, Branden Fitelson argues that a variant of Miller's argument for the language dependence of the accuracy of predictions can be applied to Joyce's notion of accuracy of credences formulated in terms of scoring rules, resulting in a general potential problem for Joyce's argument for probabilism. We argue that no relevant problem of the sort Fitelson supposes arises since his main theorem and his supporting arguments presuppose the validity of nonlinear transformations of credence functions that Joyce's theory, charitably construed, would identify as invalid on the basis of the principle of simple dominance.

The basic education of philosophers of science during the second half of the twentieth century was founded on the seven Rs: the three basic Rs and the topics of Rationality, Realism, Reductionism, and Relativism. The topic and ideas in this volume are foundational and challenging. They challenge and replace the ancient notion of philosophy as unified knowledge and the corresponding images of the tree of knowledge and encyclopedia forests.

Political science research, particularly in international relations and comparative politics, has increasingly become dominated by statistical and formal approaches. The promise of these approaches shifted the methodological emphasis away from case study research. In response, supporters of case study research argue that case studies provide evidence for causal claims that is not available through statistical and formal research methods, and many have advocated multimethod research. I propose a way of understanding the integration of multiple methodologies in which the causes sought in case studies are treated as singular causation and contingent on a theoretical framework.

Critiques of case studies as an epistemic genre usually focus on the domain of justification and hinge on comparisons with statistics and laboratory experiments. In this domain, case studies can be defended by the notion of “infirming”: they use many different bits of evidence, each of which may independently “infirm” the account. Yet their efficacy may be more powerful in the domain of discovery, in which these same different bits of evidence must be fully integrated to create an explanatory account with internal validity.

This article examines ontological/dynamical aspects of emergence, specifically the micro-macro relation in cases of universal behavior. I discuss superconductivity as an emergent phenomenon, showing why microphysical features such as Cooper pairing are not necessary for deriving characteristic properties such as infinite conductivity. I claim that the difficulties surrounding the thermodynamic limit in explaining phase transitions can be countered by showing how renormalization group techniques facilitate an understanding of the physics behind the mathematics, enabling us to clarify epistemic and ontological aspects of emergence. I close with a discussion of the impact of these issues for questions concerning natural kinds.

Philosophy of Science owes a considerable debt of gratitude to its referees—the Associate Editors, the members of the Editorial Board, and the many other individuals who help to review submissions. In acknowledgement of their essential service, we list here the names of those individuals who have refereed papers during the years 2010 and 2011.

The burgeoning science of well-being makes no secret of being value laden: improvement of well-being is its explicit goal. But in order to achieve this goal its concepts and claims need to be value adequate; that is, they need, among other things, to adequately capture well-being. In this article I consider two ways of securing this adequacy—first, by relying on philosophical theory of prudential value and, second, by the psychometric approach. I argue that neither is fully adequate and explore an alternative. This alternative requires thorough changes in the way philosophers theorize about well-being.