To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...) and the essential characteristics of scientific explanation. In the present essay, an attempt will be made to shed some light on these issues by means of an elementary survey of the basic pattern of scientific explanation and a subsequent more rigorous analysis of the concept of law and of the logical structure of explanatory arguments. (shrink)

Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication theory more than fifty years ago. Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored (...) to issue this commemorative reprinting of a classic. (shrink)

First published in 1963, this title considers the philosophical problems encountered when attempting to provide a clear and general explanation of scientific principles, and the basic confrontation between such principles and experience. Beginning with a detailed introduction that considers various approaches to the philosophy and theory of science, Israel Scheffler then divides his study into three key sections – Explanation, Significance and Confirmation – that explore how these complex issues involved have been dealt with in contemporary research. This title, by (...) one of America’s leading philosophers, will provide a valuable analysis of the theory and problems surrounding the Philosophy of Science. (shrink)

It is generally agreed that the method of explication consists in replacing a vague, presystematic notion (the explicandum) with a precise notion (the explicatum) formulated in a systematic context. However, Carnap and others who have used this and related terms appear to hold inconsistent views as to what constitutes an adequate explication. The central feature of the present explication of 'explication' is the correspondence condition: permitting the explicandum to deviate from some established "ordinary-language" conventions but, at the same time, requiring (...) that the explicatum correspond (via an effective translation) to the chosen "definitive intension" of the explicandum. (In effect, the first stages of an explication provide an informal characterization of a vague and possibly inconsistent language convention.) The present account of explication contrasts sharply with that sketched by Quine in Word and Object (although Quine accepts a correspondence condition of a sort). The terms `explication 1 ' and `explication 2 ' are used to indicate these quite different senses of the term. In Kaplan's terminology, explication 1 is intended to remedy "external vagueness" while explication 2 is intended to remedy "internal vagueness.". (shrink)

It is argued that current attempts to model human learning behavior commonly fail on one of two counts: either the model assumptions are artificially restricted so as to permit the application of mathematical techniques in deriving their consequences, or else the required complex assumptions are imbedded in computer programs whose technical details obscure the theoretical content of the model. The first failing is characteristic of so-called mathematical models of learning, while the second is characteristic of computer simulation models. An approach (...) to model building which avoids both these failings is presented under the title of a black-box theory of learning. This method permits the statement of assumptions of any desired complexity in a language which clearly exhibits their theoretical content.Section II of the paper is devoted to the problem of testing and comparing alternative learning theories. The policy advocated is to abandon attempts at hypothesis testing. It is argued that, in general, we not only lack sufficient data and sufficiently powerful techniques to test hypotheses, but that the truth of a model is not really the issue of basic interest. A given model may be true in the sense that on the basis of available evidence we cannot statistically reject it, but not interesting in the sense that it provides little information about the processes underlying behavior. Rather, we should accept or reject models on the basis of how much information they provide about the way in which subjects respond to environmental structure. This attitude toward model testing is made precise by introducing a formal measure of the information content of a model. Finally, it is argued that the statistical concept of degrees-of-freedom is misleading when used in the context of model testing and should be replaced by a measure of the information absorbed from the data in estimating parameters. (shrink)