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)

The distinction between explanation and prediction has received much attention in recent literature, but the equally important distinction between explanation and description (or between prediction and description) remains blurred. This latter distinction is particularly important in the social sciences, where probabilistic models (or theories) often play dual roles as explanatory and descriptive devices. The distinction between explanation (or prediction) and description is explicated in the present paper in terms of information theory. The explanatory (or predictive) power of a probabilistic model (...) is identified with information taken from (or transmitted by) the environment (e.g., the independent, experimentally manipulated variables), while the descriptive power of a model reflects additional information taken from (or transmitted by) the data. Although information is usually transmitted by the data in the process of estimating parameters, it turns out that the number of free parameters is not a reliable index of transmitted information. Thus, the common practice of treating parameters as degrees-of-freedom in testing probabilistic models is questionable. Finally, this information-theoretic analysis of explanation, prediction, and description suggests ways of resolving some recent controversies surrounding the pragmatic aspects of explanation and the so-called structural identity thesis. (shrink)

The aim of this paper is twofold: first to sketch a framework for classifying a wide range of conceptions of scientific objectivity and second to present and defend a conception of scientific objectivity that fills a neglected niche in the resulting hierarchy of viewpoints. Roughly speaking, the proposed ideal of scientific objectivity is effectiveness in the informal but technical sense of an effective method. Science progresses when "higher levels of communicative discourse" are reached by transforming subjective judgments regarding the generation (...) and reduction of data or the testing of theories into objective decision procedures that are automatic or mechanical. (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)

This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic (...) difference. On Greeno's view, the essence of explanation lies in the relevance relations expressed by the conditional probabilities that relate the explanans variables to the explanandum variables; on my view, in contrast, the essence of explanation lies in theories viewed as hypothetical structures which deductively entail conditional probability distributions linking the explanans variables and the explanandum variables. The explanatory power of a stochastic theory is identified with information (regarding the values of explanandum variables) which is "absorbed from" the explanans variables. While other information which is "absorbed from" the explanandum variables (through the process of parameter estimation, for example) reflects descriptive power of the theory. I prove that Greeno's measure of transmitted information is a limiting special case of the E-I model, but that the former, unlike the latter, makes no distinction between explanatory power and descriptive power. (shrink)

there be an objective modal distinction between the observable and the unobservable.’ My intent is to counter Ladyman's claim that the irreducibly modal character of empirical adequacy is something that is ‘really wrong with constructive empiricism’. I argue that disposition concepts refer to non-modal properties of types rather than to modal properties of tokens of those types. Solubility, for example, is an ‘occurrent’, though unobservable, property of a type of substance (involving the structure of associated atoms); and observability is, similarly, (...) an ‘occurrent’, though unobservable, property of a type of event (involving the structure of associated physical systems). Empirical adequacy, like truth, is an objective, semantic notion; the empirical adequacy of a theory depends upon all actual tokens of the relevant observable type, not just upon the tokens that have actually been observed. Introduction The typical character of disposition concepts Confirmational versus semantic empirical adequacy Conclusion. (shrink)

In his book, The Scientific Image, Bas van Fraassen argues for an anti-realist view of science according to which the sole epistemological aim of science is to "save the phenomena". As originally conceived, his constructive empiricism is strongly extensional, but in his account of the empirical adequacy of probabilistic theories, van Fraassen reluctantly abandons this extensional position, arguing that modal (intensional) notions are unavoidable in interpreting probability. I argue in this paper that van Fraassen has not presented the strongest possible (...) case for a fully extensional account of empirical adequacy. On the present account, a probabilistic theory is empirically adequate just in case there is no theory that has a higher likelihood relative to all the phenomena in the actual world. I prove that a theory is empirically adequate if and only if the probability attributed to each observable event that occurs in the actual world is its limiting relative frequency. Thus, in so far as one is concerned with the empirical adequacy (rather than with the literal truth) of probabilistic theories, strict relative frequencies provide an adequate basis for "interpreting probability". Probabilistic theories are treated in a genuine measurement context. In this connection I distinguish between worlds that are "strongly infinite" and worlds that are only "weakly infinite". It is characteristic of the former worlds that there is no limit to the precision of actual measurements. I show by example that in some "strongly infinite" worlds, theories will be empirically adequate only if observables are represented by continuous (rather than discrete) random variables. The range of such examples is clarified by a theorem which states necessary and sufficient conditions under which the "phenomena can be saved" without recourse to continuous random variables and real numbers. (shrink)

In his recent book Scientific Explanation and the Causal Structure of the World Wesley Salmon provides a detailed explanation of objective homogeneity, a concept which is central to his S-R model of explanation. 1 propose a modification of Salmon's definition which both simplifies and (in minor ways) corrects it, while at the same time generalizes it by including an important temporal factor that is missing from the original. I argue that if the world is irreducibly stochastic, then objective probabilities (determined (...) by objective homogeneous reference classes) must be temporally relativized. We can speak coherently of the objective probability of a particular event relative to a given point in time, but not of the objective probability of the event simpliciter. I briefly explore the consequences, of the temporal relativity of objective homogeneity for Salmon's attempt to secure an objective (nonepistemic, nonpraagmatic) S-R basis for causal explanation. (shrink)

In his recent book Scientific Explanation and the Causal Structure of the World Wesley Salmon provides a detailed explication of objective homogeneity, a concept which is central to his Statistical-Relevance (S-R) model of explanation. One of the purposes of Salmon’s explication is to refute Hempel’s thesis of the epistemic relativity of statistical explanation. According to this thesis “the concept of statistical explanation for particular events is essentially relative to a given knowledge situation” (Hempel 1965, pp. 402-403, quoted in Salmon 1984, (...) p. 48). Salmon introduces (1984, p. 55) the concept that forms the basis for his S-R model as follows: “A reference class A is homogeneous with respect to an attribute B provided there is no set of properties Ci (1 ≤i ≤ k; k ≥ 2) in terms of which A can be relevantly partitioned. (shrink)

According to van Fraassen's constructive empiricism, the epistemological aim of scientific theories is "to save the phenomena". Theories which achieve this aim are said to be empirically adequate. In an earlier paper a likelihood analysis of the empirical adequacy of simple statistical hypotheses was given. The present paper extends that likelihood analysis of empirical adequacy to composite statistical hypotheses. It is argued that for composite hypotheses the notion of likelihood is ambiguous. This ambiguity leads to a distinction between predictive adequacy, (...) based upon a prior likelihood function, and descriptive adequacy, based upon a posterior likelihood function. (shrink)

This paper argues that if the world is irreducibly stochastic, then both Salmon's S-R model of explanation and Fetzer's C-R model of explanation have the following undesirable consequence: the objective probability (associated with the model's relevance condition) of any actual macro-event is either undefined or else, if defined, it equals one--so that the event is not even a candidate for a probabilistic explanation. This result follows from the temporal ambiguity of ontic probability in an irreducibly stochastic world. It is argued (...) further that an analogous difficulty faces those theories of probabilistic causality which depend upon the notions of contributing and counteracting causes. Because of the problem of temporal ambiguity, it is not possible to objectively label a particular event as a contributing (or a counteracting) cause of some subsequent event. The argument is carried through in detail for a recent theory of probabilistic causality proposed by Paul Humphreys. (shrink)

This book brings together several strands of Salmon's important philosophical investigations, spanning two decades, into a comprehensive theory of scientific explanation. The fundamental tenet of Salmon's ontic conception of explanation is that "to explain an event is to exhibit it as occupying its... place in the discernible [causal] patterns of the world". Thus an adequate theory of explanation presupposes an account of the causal structure of the world, and one of the principal objectives of the book is to outline a (...) theory of causal processes that will provide the requisite foundation for a theory of objective scientific explanation. (shrink)