Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the “external” parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered in the internal stages of science: choice of methodology, characterization of data, and interpretation of results.

The use of informational terms is widespread in molecular and developmental biology. The usage dates back to Weismann. In both protein synthesis and in later development, genes are symbols, in that there is no necessary connection between their form (sequence) and their effects. The sequence of a gene has been determined, by past natural selection, because of the effects it produces. In biology, the use of informational terms implies intentionality, in that both the form of the signal, and the response to it, have evolved by selection. Where an engineer sees design, a biologist sees natural selection.

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

van Fraassen's constructivist empiricist account of theories makes an epistemic distinction between entities that can and cannot be observed with the naked eye. A belief about the correctness of a theoretical description of an entity that is observable with the naked eye can be warranted by a theory. In contrast, no theory can warrant a belief about the correctness of a description of an unobservable entity.

I argue that we ought to instead adopt a view that takes account of the fact that some entities that cannot be observed with the naked eye can nevertheless be observed on the basis of the same physical principle as those entities that can be. This suggests that there is a distinction different from van Fraassen's that might do the work van Fraassen intends his to do, but a distinction that is principled. Understanding why this is so suggests that his distinction is grounded merely in human chauvinism.

It is widely thought that the size of a probability makes no difference to the quality of a probabilistic explanation. I argue that explanatory practice in statistical physics, past and present, belies this claim. The claim has gained currency only because of an impoverished conception of the nature of probabilistic processes and an unwarranted assumption that all probabilistic explanations have a single form.

In many texts on evolution the reader will find a characteristic depiction of inheritance and evolution, one showing the generations of an evolving population linked only by a causal flow from genotype to genotype. On this view, the genotype of each organism in this population plays a dual role as both the motor of individual development and as the sole causal channel across the generations (cf., e.g., Maynard Smith 1993, Fig. 8). This picture is known to be literally false. In many species, parents exert direct causal influence on their offspring, and some of those influences cause the offspring to resemble the parent. For example, a butterfly that lays her eggs on the same plant host on which she hatched thereby exerts a causal influence on her offspring, and one apt to cause them to resemble her more than they would, had she chosen a plant of a different species. None of this is at all controversial, but it poses a puzzle. If the “Weissmanian” conception is literally false, why is it seen as a perspicacious representation of evolution?

Philosophers of science widely believe that the hereditarian theory about racial differences in IQ is based on methodological mistakes and confusions involving the concept of heritability. I argue that this “received view” is wrong: methodological criticisms popular among philosophers are seriously misconceived, and the discussion in philosophy of science about these matters is largely disconnected from the real, empirically complex issues debated in science.

The role played by the concept of genetic coding in biology is discussed. I argue that this concept makes a real contribution to solving a specific problem in cell biology. But attempts to make the idea of genetic coding do theoretical work elsewhere in biology, and in philosophy of biology, are probably mistaken. In particular, the concept of genetic coding should not be used (as it often is) to express a distinction between the traits of whole organisms that are coded for in the genes and the traits that are not.

In The Logic of Decision Richard Jeffrey defends a version of expected utility theory that advises agents to choose acts with an eye to securing evidence for thinking that desirable results will ensue. Proponents of “causal” decision theory have argued that Jeffrey's account is inadequate because it fails to properly discriminate the causal features of acts from their merely evidential properties. Jeffrey's approach has also been criticized on the grounds that it makes it impossible to extract a unique probability/utility representation from a sufficiently rich system of preferences (given a zero and unit for measuring utility). The existence of these problems should not blind us to the fact that Jeffrey's system has advantages that no other decision theory can match: it can be underwritten by a particularly compelling representation theorem proved by Ethan Bolker; and it has a property called partition invariance that every reasonable theory of rational choice must possess. I shall argue that the non-uniqueness problem can be finessed, and that it is impossible to adequately formulate causal decision theory, or any other, without using Jeffrey's theory as one's basic analysis of rational desire.

The representation theorems of expected utility theory show that having certain types of preferences is both necessary and sufficient for being representable as having subjective probabilities. However, unless the expected utility framework is simply assumed, such preferences are also consistent with being representable as having degrees of belief that do not obey the laws of probability. This fact shows that being representable as having subjective probabilities is not necessarily the same as having subjective probabilities. Probabilism can be defended on the basis of the representation theorems only if attributions of degrees of belief are understood either antirealistically or purely qualitatively, or if the representation theorems are supplemented by arguments based on other considerations (simplicity, consilience, and so on) that single out the representation of a person as having subjective probabilities as the only true representation of the mental state of any person whose preferences conform to the axioms of expected utility theory.

When philosophers of physics explore the nature of chance, they usually look to quantum mechanics. When philosophers of biology explore the nature of chance, they usually look to microevolutionary phenomena such as mutation or random drift. What has been largely overlooked is the role of chance in macroevolution. The stochastic models of paleobiology employ conceptions of chance that are similar to those at the microevolutionary level, yet different from the conceptions of chance often associated with quantum mechanics and Laplacean determinism.

Maynard Smith is right that one of the most striking features of contemporary biology is the ever-increasing prominence of the concept of information, along with related concepts like representation, programming, and coding. Maynard Smith is also right that this is surely a phenomenon which philosophers of science should examine closely. We should try to understand exactly what sorts of theoretical commitment (if any) are made when biological systems are described in these terms, and what connection there is between semantic descriptions in biology and in other domains.

There is no set Δ of probability axioms that meets the following three desiderata:

• (1) Δ is vindicated by a Dutch book theorem;

• (2) Δ does not imply regularity (and thus allows, among other things, updating by conditionalization);

• (3) Δ constrains the conditional probability q(·, z) even when the unconditional probability p(z) (= q(z, T)) equals 0.

• This has significant consequences for Bayesian epistemology, some of which are discussed.

In this paper I discuss some of the mathematics behind an often quoted existence theorem from Richard Jeffrey's The Logic of Decision (Jeffrey 1990) in order to pose several new questions about the meaning and value of that mathematics for decision theory.

Maynard Smith notes that he provides a natural history and not a philosophical analysis of the use of concepts of information in contemporary biology. Just a natural history, however rich, would do little to resolve the ongoing controversy about the role of these concepts in biology. None of the disputants deny that the biological use of these concepts is pervasive. The dispute is about whether these concepts—and the framework in which they are embedded—continue to be of explanatory value in contemporary biology. Fortunately, Maynard Smith does much more than provide a natural history: his contribution is also a sustained attempt to justify many (though not all) of the uses of information in biology.

One of the issues dividing “absolutists” and “relationists” is the question whether all motion is relative motion or, in the words of Earman, spacetime has “structures that support absolute quantities of motion.” This paper argues that, despite the enormous literature bearing on the topic, it is problematic to formulate a general criterion for when a quantity counts as absolute in contrast to merely relative in a way that is not hopelessly parasitic on other, presumably distinct, senses of “absolute.” Furthermore, I suggest that the vicissitudes of the evolution of the concept of absolute motion have contributed to this difficulty.

No one has a well developed solution to Duhem's problem, the problem of how experimental evidence warrants revision of our theories. Deborah Mayo proposes a solution to Duhem's problem in route to her more ambitious program of providing a philosophical account of inductive inference and experimental knowledge. This paper is a response to Mayo's Error Statistics (ES) program, paying particular attention to her response to Duhem's problem. It turns out that Mayo's purported solution to Duhem's problem is very significant to her project, for the epistemic license claimed by ES and the philosophical underpinnings to her account of experimental knowledge depend on this solution. By introducing the partition problem, I argue that ES fails to solve Duhem's problem and therefore fails to provide an adequate account of experimental knowledge.

Arguments in favor of anti-representationalism in cognitive science often suffer from a lack of attention to detail. The purpose of this paper is to fill in the gaps in these arguments, and in so doing show that at least one form of anti-representationalism is potentially viable. After giving a teleological definition of representation and applying it to a few models that have inspired anti-representationalist claims, I argue that anti-representationalism must be divided into two distinct theses, one ontological, one epistemological. Given the assumptions that define the debate, I give reason to think that the ontological thesis is false. I then argue that the epistemological thesis might, in the end, turn out to be true, despite a potentially serious difficulty. Along the way, there will be a brief detour to discuss a controversy from early twentieth century physics.

In this paper Richard Jeffrey's ‘Logic of Decision’ is extended by examination of agents' attitudes to the sorts of possibilities identified by indicative conditional sentences. An expression for the desirability of conditionals is proposed and, along with Adams' thesis that the probability of a conditional equals the conditional probability of its antecedent given its consequent, is defended by informally deriving it from Jeffrey's notion of desirability and some weak constraints on rational preference for conditional possibilities. Finally a statement is given of a representation theorem establishing the conditions under which a rational agent's preferences for conditionals determines the existence of unique measures (up to choice of scale) of her degrees of belief and desire.