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.

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.

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.

I compare recent work in the sociology of scientific knowledge with other types of sociological research. On this basis I urge a revival of the sociology of science, offer a tentative agenda, and attempt to show how the questions I raise might be addressed.

Protagonists in the so-called Science Wars differ most markedly in their views about the role of values in science and what makes science valuable. Scientists and philosophers of science have traditionally considered the principal aims of science to be explanation and application. Only cognitive values should influence what is taken to be explanatory. Social and political values affect the priority assigned to various scientific problems and the ways in which scientific results are applied. Ethical considerations may be brought to bear on the treatment of human and animal subjects, and the manner in which scientific results are communicated.

Recent critiques of science allege that the content of scientific explanations reflects the dominant ideology and interests of scientists and their patrons. Instead of calling for more value neutrality, some now urge that science take as a principal aim the emancipation of oppressed subcultures. Not only should progressive political values be allowed to set the problems attempted, they also should be used to constrain the types of answers which are pursued. Since scientific knowledge is constructed by us, we should take responsibility for its content.

This paper argues that the project of Emancipationist science is impractical and self-defeating. There is good reason to believe that there would be unresolvable political disputes concerning which kinds of scientific theories are truly emancipiatory. Furthermore, just as placebos cease to work when recognized as such, so would a science known to be constrained by political considerations lose its special epistemic authority.

Obviously we could have failed to be successful scientists. But a serious question lurks beneath the banal one stated in my title. If the results of a scientific investigation are correct, would any investigation of roughly the same subject matter, if successful, at least implicitly contain or imply the same results? Using examples ranging from immunology to high-energy physics, the paper presents the cases for both positive and negative answers. The paper is deliberately non-conclusive, arguing that the question is one of the few serious philosophical issues that divides protagonists in the “science wars.”

Two decades of critique have sensitized historians and philosophers of science to the inadequacies of conventional dichotomies between theory and practice, thereby prompting the search for new ways of writing about science that are less beholden than the old ways to the epistemological mores of theoretical physics, and more faithful to the actual practices not only of physics but of all the natural sciences. The need for alternative descriptions seems particularly urgent if one is to understand the place of theory (and, in parallel, the role of modeling) in contemporary molecular biology, a science where, until now, no division between theory and experiment has obtained, and where distinctions between representing and intervening, and more generally, between basic and applied science, are daily becoming more blurred.

Indeed, the very division between theory and experiment threatens to slight the extensive and sophisticated theoretical analyses (and even modeling) on which experimental work in contemporary molecular biology so often depends. My aim in this paper is to find a way of talking about theoretical practices in biology that is directly rooted in the mix of conceptual and material work that biologists do. As an example of such theoretical practices, I choose for the focus of my analysis the development of a model for gene regulation out of the experimental work of Eric Davidson and his colleagues at Cal Tech.

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.

The positivistic Received View construed scientific theories syntactically as axiomatic calculi where theoretical terms were given a partial semantic interpretation via correspondence rules connecting them to observation statements. This paper assesses what, with hindsight, seem the most important defects in the Received View; surveys the main proposed successor analyses to the Received View—various Semantic Conception versions and the Structuralist Analysis; evaluates how well they avoid those defects; examines what new problems they face and where the most promising require further development or leave unanswered questions; explores implications of recent work on models for understanding theories; and rebuts the few criticisms of the Semantic Conception that have surfaced.

Thirty years after the conference that gave rise to The Structure of Scientific Theories, there is renewed interest in the nature of theories and models. However, certain crucial issues from thirty years ago are reprised in current discussions; specifically: whether the diversity of models in the science can be captured by some unitary account; and whether the temporal dimension of scientific practice can be represented by such an account. After reviewing recent developments we suggest that these issues can be accommodated within the partial structures formulation of the semantic or model-theoretic approach.

Since the collapse of the ‘received view’ consensus in the late 1960s, the question of scientific realism has been a major preoccupation of philosophers of science. This paper sketches the history of this debate, which grew from developments in the philosophy of language, but eventually took on an autonomous existence. More recently, the debate has tended towards more ‘local’ considerations of particular scientific episodes as a way of getting purchase on the issues. The paper reviews two such approaches, Fine's and Hacking's, describing their positions, their prospects, and how they are related. Finally, the paper suggests that local philosophies of science offer a way for our discipline to engage more fruitfully with the public and the scientific community.

Logical Empiricism is commonly regarded as uninterested in, if not hostile to sociological investigations of science. This paper reconstructs the views of Otto Neurath and Philipp Frank on the legitimacy and relevance of sociological investigations of theory choice. It is argued that while there obtains a surprising degree of convergence between their programmatic pronouncements and the Strong Programme, the two types of project nevertheless remain distinct. The key to this difference lies in the different assessment of a supposed dilemma facing post-Mertonian sociologists of science.

This essay explores some of the issues raised as regards the relations of philosophy and sociology of science in the work of Rudolf Carnap and Hans Reichenbach. It argues that Hans Reichenbach's distinction between the contexts of discovery and justification should not be seen as erecting a principled normative/descriptive distinction that demarcates philosophy of science from sociology of science. The essay also raises certain issues about the role of volition, decision, and the limits of epistemological concern in the work of Carnap and Reichenbach and in some recent sociology of science. The relations of philosophy of science to sociology of science are seen to be more deeply rooted and more interesting than the Science Warriors would have us believe.

This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.

There are two reasons, I claim, scientists do and should ignore standard philosophical theories of objective evidence: (1) Such theories propose concepts that are far too weak to give scientists what they want from evidence, viz., a good reason to believe a hypothesis; and (2) They provide concepts that make the evidential relationship a priori, whereas typically establishing an evidential claim requires empirical investigation.

In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a new and more adequate philosophy of evidence can move.

In this paper we examine the pattern of conceptual change during scientific revolutions by using methods from cognitive psychology. We show that the changes characteristic of scientific revolutions, especially taxonomic changes, can occur in a continuous manner. Using the frame model of concept representation to capture structural relations within concepts and the direct links between concept and taxonomy, we develop an account of conceptual change in science that more adequately reflects the current understanding that episodes like the Copernican revolution are not always abrupt. When concepts are represented by frames, the transformation from one taxonomy to another can be achieved in a piecemeal fashion not preconditioned by a crisis stage, and a new taxonomy can arise naturally out of the old frame instead of emerging separately from the existing conceptual system. This cognitive mechanism of continuous change demonstrates the constructive roles of anomaly and incommensurability in promoting the progress of science.

Thomas Kuhn's The Structure of Scientific Revolutions was published at the beginning of what has come to be known as “the cognitive revolution.” With hindsight one can construct significant parallels between the problems of knowledge, perception, and learning with which Kuhn and cognitive scientists were grappling and between the accounts developed by each. However, by and large Kuhn never utilized the research in cognitive science—especially in cognitive psychology—that we believe would have furthered his own paradigm. This is puzzling since he did not have the traditional philosophical aversion to “psychologizing” and in fact drew on insights from psychology to support the most radical claims in Structure, such as the “Gestalt switch” nature of conceptual change. Indeed, the research program outlined there seems intrinsically historical, philosophical, and psychological and Kuhn's work has had considerable influence on research in cognitive science.

Looking at Thomas Kuhn's work from a cognitive science perspective helps to articulate and to legitimize, to some degree, his rejection of traditional views of concepts, categorization, theory structure, and rule-based problem solving. Whereas my colleagues focus on the later Kuhn of the MIT years, I study the early Kuhn as an anticipation of case-based reasoning and schema theory. These recent developments in cognitive psychology and artificial intelligence may point toward a more computational version of Kuhn's ideas, but they also expose ambiguities in his work, notably in his understanding of exemplars.