The question of coherence of rules for changing degrees of belief in the light of new evidence is studied, with special attention being given to cases in which evidence is uncertain. Belief change by the rule of conditionalization on an appropriate proposition and belief change by “probability kinematics“ on an appropriate partition are shown to have like status.

This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables Si and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions in the systems to which T applies or the magnitudes of the conditional probabilities P(Mj/Si), in the manner in which IT does.

In this paper I wish to argue that counterfactual analyses of causation are inadequate. I believe the counterfactuals that are involved in counterfactual analyses of causation are often false, and thus the theories do not provide an adequate account of causation. This is demonstrated by the presentation of a counterexample to the counterfactual analyses of causation. I then present a unified theory of causation that is based upon probability and counterfactuals. This theory accounts for both deterministic and indeterministic causation, and is not subject to many of the traditional problems facing theories of causation.

Several contemporary philosophers, like G. J. Whitrow, argue that it is logically impossible for the past to be infinite, and offer several arguments in support of this thesis. I believe their arguments are unsuccessful and aim to refute six of them in the six sections of the paper. One of my main criticisms concerns their supposition that an infinite series of past events must contain some events separated from the present event by an infinite number of intermediate events, and consequently that from one of these infinitely distant past events the present could never have been reached. I introduce several considerations to show that an infinite series of past events need not contain any events separated from the present event by an infinite number of intermediate events.

L. Jonathan Cohen has written a number of important books and articles in which he argues that mathematical probability provides a poor model of much of what paradigmatically passes for sound reasoning, whether this be in the sciences, in common discourse, or in the law. In his book, The Probable and the Provable, Cohen elaborates six paradoxes faced by advocates of mathematical probability (PM) when treating issues of evidence as they would arise in a court of law. He argues that his system of inductive probability (PI) satisfactorily handles the issues that proved paradoxical for mathematical probability, and consequently PI deserves to be thought of as an important standard of rational thinking. I argue that a careful look at each of the alleged paradoxes shows that there is no conflict between mathematical probability and the law, except when for reasons of policy we opt for values in addition to accuracy maximization. Recognizing the role of such policies provides no basis for questioning the adequacy of PM. The significance of this critical treatment of Cohen's work is that those interested in revising the laws of evidence to allow for more explicitly mathematical approaches ought to feel that such revisions will not violate the spirit of forensic rationality.

Ferdinand Schoeman (1986) attempts to refute six arguments that I gave in The Probable and the Provable (1977, pp. 49–120) for not analyzing the standards of forensic proof in terms of any concept of probability that conforms to the familiar principles of the mathematical calculus of chance. But he evidently does not appreciate the strength of the case for rejecting the validity of any such analysis. In the course of discussing his criticisms, I shall add a seventh argument to fortify my position, though I concede that the second of my original arguments has to be discarded.

Is sociobiology a new paradigm? A number of people have claimed that it is. I argue that, sociologically speaking, it may well be. But epistemologically, it is not. The case rests on one's interpretation of the major Darwinian evolutionary mechanism, natural selection. In this note, it is shown that sociobiology relies on an orthodox understanding of selection. Thus, in crucial epistemological respects, sociobiology is continuous with the rest of Darwinian evolutionary theory.

John Dupré (1984) has recently criticized the theory of probabilistic causality developed by, among others, Good (1961–62), Suppes (1970), Cartwright (1979), and Skyrms (1980). He argues that there is a tension or incompatibility between one of its central requirements for the presence of a causal connection, on the one hand, and a feature of the theory pointed out by Elliott Sober and me (1983), on the other. He also argues that the requirement just alluded to should be given up. I defend the theory against Dupré‘s criticisms and conclude with comments on Dupré‘s appraisal of the bearing of his arguments on the nature of probabilistic causal laws.

The aim of Stich's book is to further the controversial thesis that the conceptual framework of ‘folk’ psychology will have no significant role to play in a mature cognitive science. Skepticism about the scientific relevance of folk psychology has been voiced by others (behaviorists and nonbehaviorists alike); but Stich's critique is both novel and more fully developed than earlier ones. The charge is not–-or not simply–-that ‘folk theory’ is a “degenerating paradigm“ (Churchlands), or that, in general, the constructs of folk theory fail to refer altogether (Rorty; Dennett). Stich's thesis is subtler, and rests on the claim that the individuation of folk-psychological states is irremediably vague and context-and-observer relative in a way that makes a folk-psychological taxonomy ill-suited to the requirements of scientific explanation and systematization. Stich, of course, does not reject mentalism as such, as did the behaviorists: he merely rejects the assumption–-often tacitly accepted by cognitive scientists and by their philosophical interpreters–-that the mental states posited by a mature cognitive science will essentially correspond to the intentional, propositional attitude states of folk psychology. Thus, for Stich, there is no reason to suppose that cognitive science will turn out to be a sophisticated extension of folk psychology. The “Panglossian prospect” (p. 5) of a conceptual reunification of the scientific image of mind with the ‘manifest’ image under the banner of cognitivism is just what this book calls into question.