I consider the question of how literally one can construe the “gauge argument,” which is the canonical means of understanding the putatively central import of local gauge symmetry principles for fundamental physics. As I argue, the gauge argument must be afforded (at best) a heuristic reading. Claims to the effect that the argument reflects a deep “logic of nature” must, for numerous reasons I discuss, be taken with a grain of salt.

The idea that rational agents should have acyclic preferences and should obey conditionalization has been defended on the grounds that otherwise an agent is threatened with becoming a “money pump.” This essay argues that such arguments fail to prove their claims.

It has long been known that the practice of testing all hypotheses at the same level (such as 0.05), regardless of the distribution of the data, is not consistent with Bayesian expected utility maximization. According to de Finetti's “Dutch Book” argument, procedures that are not consistent with expected utility maximization are incoherent and they lead to gambles that are sure to lose no matter what happens. In this paper, we use a method to measure the rate at which incoherent procedures are sure to lose, so that we can distinguish slightly incoherent procedures from grossly incoherent ones. We present an analysis of testing a simple hypothesis against a simple alternative as a case-study of how the method can work.

This paper is an attempt to spell out what makes the scientific study of pain so distinctive from a philosophical perspective. Using the IASP definition of ‘pain’ (1986) as our guide, we raise a number of questions about the philosophical assumptions underlying the scientific study of pain. We argue that unlike the study of ordinary perception, the study of pain focuses from the very start on the experience itself and its qualities, without making deep assumptions about whether pain experiences are perceptual. This in turn puts scientific explanation in a curious position due to pain's inherently subjective epistemic nature. The reason for this focus on the experience itself and its qualities, we argue, has to do with pain's complex phenomenology involving an affective/motivational dimension. We argue for the scientific legitimacy of first-person phenomenological studies and attempts to correlate phenomenology with neural events. We argue that this methodological procedure is inevitable and has no anti-physicalist ontological implications when properly understood. We end the paper by commenting on a discussion between two prominent pain scientists in the field, Don Price and Howard Fields, about the need to distinguish more dimensions in the phenomenology of pain and how to classify them vis-à-vis the recent scientific findings. Our interest in this discussion is not only to introduce some clarifications but also to show how “neurophenomenology” has already been shaping the scientific research and to back our claim about why this methodology is inevitable with an example.

This paper attacks the perennial philosophical and scientific quest for a simple and unified vision of the world. Without denying the attraction of this vision, I argue that such a goal often seriously distorts our understanding of complex phenomena. The argument is illustrated with reference to simplistic attempts to provide extremely general views of biology, and especially of human nature, through the theory of evolution. Although that theory is a fundamental ingredient of our scientific world view, it provides only one of a number of perspectives that are required for an understanding of biology in general, and human behaviour in paticular. The argument is connected to the replacement of views of science in terms of universal laws with views that emphasise ranges of models more locally suited to specific phenomena.

Phylogenetic information is often necessary to distinguish between evolutionary scenarios. Recently, some prominent proponents of evolutionary psychology have acknowledged this, and have claimed that such evidence has in fact been brought to bear on adaptive hypotheses involving complex human psychological traits. Were this possible, it would be a valuable source of evidence regarding hypothesized adaptive traits in humans. However, the structure of the Hominidae family makes this difficult or impossible. For many traits of interest, the closest extant relatives to the human species are too phenotypically different from humans for such methods to provide meaningful data. While phylogenetic information can be useful for testing adaptive hypotheses in humans, these generally involve traits that are (a) not widely shared in the species or (b) fairly widely shared in the Hominidae family, and hence likely of a lower order of complexity than the sorts of traits evolutionary psychology has so far been interested in.

We tend to think that there are different types of biological taxa: some taxa are species, others are genera, while others are families. Linnaeus gave us his ranks in 1731. Biological theory has changed since Linnaeus's time. Nevertheless, the vast majority of biologists still assign Linnaean ranks to taxa, even though that practice is at odds with evolutionary theory and even though it causes a number of practical problems. The Linnaean ranks should be abandoned and alternative methods for displaying the hierarchical relations of taxa should be adopted.

Evidence-Based Medicine is a relatively new movement that seeks to put clinical medicine on a firmer scientific footing. I take it as uncontroversial that medical practice should be based on best evidence—the interesting questions concern the details. This paper tries to move towards a coherent and unified account of best evidence in medicine, by exploring in particular the EBM position on RCTs (randomized controlled trials).

A model of inductive inquiry is defined within the context of first-order logic. The model conceives of inquiry as a game between Nature and a scientist. To begin the game, a nonlogical vocabulary is agreed upon by the two players, along with a partition of a class of countable structures for that vocabulary. Next, Nature secretly chooses one structure (“the real world”) from some cell of the partition. She then presents the scientist with a sequence of facts about the chosen structure. With each new datum the scientist announces a guess about the cell to which the chosen structure belongs. To succeed in his or her inquiry, the scientist's successive conjectures must be correct all but finitely often, that is, the conjectures must converge in the limit to the correct cell. Different kinds of scientists can be investigated within this framework. At opposite ends of the spectrum are dumb scientists that rely on the strategy of “induction by enumeration,” and smart scientists that rely on an operator of belief revision. We report some results about the scope and limits of these two inductive strategies.

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification.

Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/backward chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.