The development of science has been a distinctive feature of human history in recent times, especially in the seventeenth and eighteenth centuries. In light of the problems that define the philosophy of science today, James Fetzer provides a foundation for inquiry into the nature of science, the history of science, and the relationship between the two. In Philosophy of Science, Fetzer investigates the aim and methods of empirical science and examines the importance of methodological commitments to the study of science (...) and the significance of interpretations of probability for understanding laws of nature and scientific explanations. This book offers a welcome framework for investigating the most recent work and the most promising solutions to the central problems that arise within this discipline. The result is a timely introduction to an increasingly important field. -- Publisher description. (shrink)

A logic of questions and answers exists within the logic of statements, if we make the following identifications (roughly): "Whether" questions are identified with true exclusive disjunctions, and "which" questions are identified with true existential quantifications. The question-and-answer process is interpreted as an information-matching game. The question mark is not needed except as a device of abbreviation. Complete and partial answers can be distinguished and various relations of relevance, independence, and resolution defined.

Conclusions reached using common sense reasoning from a set of premises are often subsequently revised when additional premises are added. Because we do not always accept previous conclusions in light of subsequent information, common sense reasoning is said to be nonmonotonic. But in the standard formal systems usually studied by logicians, if a conclusion follows from a set of premises, that same conclusion still follows no matter how the premise set is augmented; that is, the consequence relations of standard logics (...) are monotonic. Much recent research in AI has been devoted to the attempt to develop nonmonotonic logics. After some motivational material, we give four formal proofs that there can be no nonmonotonic consequence relation that is characterized by universal constraints on rational belief structures. In other words, a nonmonotonic consequence relation that corresponds to universal principles of rational belief is impossible. We show that the nonmonotonicity of common sense reasoning is a function of the way we use logic, not a function of the logic we use. We give several examples of how nonmonotonic reasoning systems may be based on monotonic logics. (shrink)

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book (...) will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science. (shrink)

This general type of view may be characterized more fully by using the notion of an inductive method. All scientists use approximately the same inductive method, which we will call the standard inductive method. This method is based on the rule of induction by simple enumeration, which may be roughly stated as follows: if it is known only that a certain property Ψ has accompanied another property Φ in a number of instances, then the larger this number of instances the (...) higher the probability that the next occurrence of Φ will be accompanied by Ψ. Though scientists actually reason inductively in accord with this rule, it is important that there are logically consistent inductive methods based on alternative rules. We will describe two of these briefly. The inverse inductive method assigns probabilities according to the rule that if it is known only that Ψ has accompanied Φ a number of times, then the larger the number of instances the lower the probability that the next occurrence of Φ will be accompanied by Ψ. The random predictive method assigns to the proposition that the next occurrence of Φ will be accompanied by Ψ a probability completely independent of the number of times that Ψ has accompanied Φ in the past.. (shrink)

Concepts and problems; The calculus of inductive probability; Alternative inductive logics and the justification of induction; Probability and action; The pragmatic theory of inductive probability; The logic of causal statements as a formal language; The logic of causal statements as a model of natural language; The dispositional theory of empirical probability; Cause and chance in space - time systems; The presupposition of theory induction; Chance, cause, and reason.

2. Presupposition is a relation between two entities which have different ontological status. Presupposition is transitive.