Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one's conditional beliefs untouched (the ‘Rigidity’ condition). Although Richard Jeffrey denied the general applicability of this condition, one of his main contributions to probabilistic thinking is a form of belief updating—now typically called ‘Jeffrey conditioning’ or ‘probability kinematics’—that is appropriate in circumstances in which Rigidity is satisfied, but where the interaction causes one to reevaluate one's probability judgments over some partition of the possibility space without conferring certainty on any particular element. The most familiar occasion for Jeffrey conditioning is receipt of uncertain evidence: things partially perceived or remembered. But it also serves to illuminate belief updating occasioned by a change in one's degrees of conditional belief, a kind of belief change largely ignored by classical Bayesianism. I argue that such changes in conditional belief can also be basic (in the sense of not being analyzable as a consequence of conditioning on factual information) and offer a kinematical model for a particular kind change in conditional belief. Both are applied to changes in preference. Finally, I argue that Rigidity can fail when changes of belief give inferential grounds for changes in conditional belief (and vice versa). These failures show that conditioning methods are properly regarded, not as valid rules of inference, but as tools in the ‘art of judgment’.

In recent years Sir Karl Popper has been turning his attention more and more towards philosophical problems arising from biology, particularly evolutionary biology. Popper suggests that perhaps neo-Darwinian evolutionary theory is better categorized as a metaphysical research program than as a scientific theory. In this paper it is argued that Popper can draw his conclusions only because he is abysmally ignorant of the current status of biological thought and that Popper's criticisms of biology are without force and his suggestions for its improvement are without need. Also it is suggested that Popper's desire to see scientific theory growth as being in some sense evolutionary may have led him astray about biology. And conversely it is suggested that since his claims about biology are not well taken his analysis of theory growth may well bear reexamination.

A different way of thinking about how the sciences are organized is suggested by the use of cross-disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form of selective realism is described which denies that one can simply read the ontological commitments from the theory itself.

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.

The argument from analogy is examined from the point of view of Carnap's confirmation theory. It is argued that if inductive arguments are to be applicable to the real world, they must contain elementary analogical inferences. Carnap's system as originally developed (the λ-system) is not strong enough to take account of analogical arguments, but it is shown that the new system, which he has announced but not published in detail (the η-system), is capable of satisfying the conditions of inductive analogy. Finally it is shown that an elementary analysis of analogical inference yields postulates of the η-system with a minimum of arbitrary assumptions.

The theory of the economics of research played a central role in the analysis of scientific method of Charles Sanders Peirce. The present paper describes Peirce's project as he saw it and then puts its machinery to work in an analysis of current issues in the philosophy of science. The aim is to show that, even apart from their historical interest, Peirce's ideas on this subject have a substantial systematic interest.

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.

Although many philosophers have employed the distinction between “direct” and “indirect” roles for values in science, I argue that it merits further clarification. The distinction can be formulated in several ways: as a logical point, as a distinction between epistemic attitudes, or as a clarification of different consequences associated with accepting scientific claims. Moreover, it can serve either as part of a normative ideal or as a tool for policing how values influence science. While various formulations of the distinction may (with further clarification) contribute to a normative ideal, they have limited effectiveness for regulating how values influence science.

The underdetermination of theory by evidence must be distinguished from holism. The latter is a doctrine about the testing of scientific hypotheses; the former is a thesis about empirically adequate logically incompatible global theories or “systems of the world”. The distinction is crucial for an adequate assessment of the underdetermination thesis. The paper shows how some treatments of underdetermination are vitiated by failure to observe this distinction, and identifies some necessary conditions for the existence of multiple empirically equivalent global theories. We consider how empiricists should respond to the possibility of such systems of the world.

Skipper and Millstein (2005) argued that existing conceptions of mechanisms failed to “get at” natural selection but left open the possibility that a refined conception of mechanisms could resolve the problems that they identified. I respond to Skipper and Millstein, and argue that while many of their points have merit, their objections can be overcome and that natural selection can be characterized as a mechanism. In making this argument, I discuss the role of regularity in mechanisms, and develop an account of stochastic (i.e., probabilistic) mechanisms. Explaining the phenomenon of adaptation through the mechanism of natural selection illustrates the power and flexibility of using mechanistic strategies to explain natural phenomena.

At first sight the Aharonov-Bohm effect appears nonlocal, though not in the way EPR/ Bell correlations are generally acknowledged to be nonlocal. This paper applies an analysis of nonlocality to the Aharonov-Bohm effect to show that its peculiarities may be blamed either on a failure of a principle of local action or on a failure of a principle of separability. Different interpretations of quantum mechanics disagree on how blame should be allocated. The parallel between the Aharonov-Bohm effect and violations of Bell inequalities turns out to be so close that a balanced assessment of the nature and significance of quantum nonlocality requires a detailed study of both effects.

This book consists thematically of three broad sections: (1) a lengthy introduction (approximately one third of the book) in which Grünbaum critically assesses the hermeneutic construal of psychoanalysis, as represented in the work of Habermas, G. S. Klein, and Ricoeur; (2) a critical examination of Popper's assessment of both psychoanalysis and inductivism; and (3) a logical analysis of core psychoanalytic ideas that constitute the foundation for much of psychoanalytic theory. This last section is, in my view, the heart of the book and therefore, it is that section on which I will focus in this review.

The epistemic state of complete ignorance is not a probability distribution. In it, we assign the same, unique, ignorance degree of belief to any contingent outcome and each of its contingent, disjunctive parts. That this is the appropriate way to represent complete ignorance is established by two instruments, each individually strong enough to identify this state. They are the principle of indifference (PI) and the notion that ignorance is invariant under certain redescriptions of the outcome space, here developed into the ‘principle of invariance of ignorance’ (PII). Both instruments are so innocuous as almost to be platitudes. Yet the literature in probabilistic epistemology has misdiagnosed them as paradoxical or defective since they generate inconsistencies when conjoined with the assumption that an epistemic state must be a probability distribution. To underscore the need to drop this assumption, I express PII in its most defensible form as relating symmetric descriptions and show that paradoxes still arise if we assume the ignorance state to be a probability distribution.

Introduction. During the past two decades philosophers of psychology have considered a large variety of computational models for philosophy of mind and more recently for cognitive science. Among the suggested models are computer programs, Turing machines, pushdown automata, linear bounded automata, finite state automata and sequential machines. Many philosophers have found finite state automata models to be the most appealing, for various reasons, although there has been no shortage of defenders of programs and Turing machines. A paper by Arthur Burks (1973) convinced me long ago that “all natural human functions” are, or can be fruitfully modeled to be, finite state automata with output. Further work in the field has reinforced this conviction. There is room, however, for the use of any of the above models in philosophy of mind and in the ongoing development of cognitive science.

In many fields in the life sciences investigators refer to downward or top-down causal effects. Craver and I defended the view that such cases should be understood in terms of a constitution relation between levels in a mechanism and intralevel causal relations (occurring at any level). We did not, however, specify when entities constitute a higher-level mechanism. In this article I appeal to graph-theoretic representations of networks, now widely employed in systems biology and neuroscience, and associate mechanisms with modules that exhibit high clustering. As a result of interconnections within clusters, mechanisms often exhibit complex dynamic behaviors that constrain how individual components respond to external inputs, a central feature of top-down causation.

Cognitive psychologists, like biologists, frequently describe mechanisms when explaining phenomena. Unlike biologists, who can often trace material transformations to identify operations, psychologists face a more daunting task in identifying operations that transform information. Behavior provides little guidance as to the nature of the operations involved. While not itself revealing the operations, identification of brain areas involved in psychological mechanisms can help constrain attempts to characterize the operations. In current memory research, evidence that the same brain areas are involved in what are often taken to be different memory phenomena or in other cognitive phenomena is playing such a heuristic function.

In Science, Truth, and Democracy, Philip Kitcher develops the notion of well-ordered science: scientific inquiry whose research agenda and applications (but not methods) are subject to public control guided by democratic deliberation. Kitcher's primary departure from his earlier views involves rejecting the idea that there is any single standard of scientific significance. The context-dependence of scientific significance opens up many normative issues to philosophical investigation and to resolution through democratic processes. Although some readers will feel Kitcher has not moved far enough from earlier epistemological positions, the book does represent an important addition to literature on science, society, and values.

Marc Ereshefsky argues that pluralism about species suggests that the species concept is not theoretically useful. It is to be abandoned in favor of several concrete species concepts that denote real categories. While accepting species pluralism, the present paper rejects eliminativism about the species category. Based on the idea that the species concept is a so-called investigative kind concept, it is argued that the species concept is important and that it is possible to make sense of a general species concept despite the existence of different concrete species concepts.

A number of authors argue that while species are evolutionary units, individuals and real entities, higher taxa are not. I argue that drawing the divide between species and higher taxa along such lines has not been successful. Common conceptions of evolutionary units either include or exclude both types of taxa. Most species, like all higher taxa, are not individuals, but historical entities. Furthermore, higher taxa are neither more nor less real than species. None of this implies that there is no distinction between species and higher taxa; the point is that such a distinction is more subtle than many authors have claimed.