Does the viability of the discovery program depend on showing either (1) that methods of generating new problem solutions, per se, have special probative weight (the per se thesis); or, (2) that the original conception of an idea is logically continuous with its justification (anti-divorce thesis)? Many writers have identified these as the key issues of the discovery debate. McLaughlin, Pera, and others recently have defended the discovery program by attacking the divorce thesis, while Laudan has attacked the discovery program by rejecting the per se thesis. This disagreement over the central issue has led to communication breakdown. I contend that both friends and foes of discovery mistake the central issues. Recognizing a form of divorce helps rather than hurts the discovery program. However, the per se thesis is not essential to the program (nor is the related debate over novel prediction); hence, the status of the per se thesis is a side issue. With these clarifications in hand, we can proceed to the next stage of the discovery debate–the development (or revival) of a generative conception of justification which goes beyond consequentialism to forge a strong linkage of generation (or rather, generatability) with justification.

While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave–-where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, view the task of statistical methods as providing appropriate measures of the evidential-strength that data affords hypotheses. Since tests appropriate for the behavioral-decision task fail to provide measures of evidential-strength, philosophers of statistics claim the use of orthodox tests in science is misleading and unjustified. What critics of orthodox tests overlook, I argue, is that the primary function of statistical tests in science is neither to decide how to behave nor to assign measures of evidential strength to hypotheses. Rather, tests provide a tool for using incomplete data to learn about the process that generated it. This they do, I show, by providing a standard for distinguishing differences (between observed and hypothesized results) due to accidental or trivial errors from those due to systematic or substantively important discrepancies. I propose a reinterpretation of a commonly used orthodox test to make this learning model of tests explicit.

In arguing a “role for history,“ Kuhn was proposing a naturalized philosophy of science. That, I argue, is the only viable approach to the philosophy of science. I begin by exhibiting the main general objections to a naturalistic approach. These objections, I suggest, are equally powerful against nonnaturalistic accounts. I review the failure of two nonnaturalistic approaches, methodological foundationism (Carnap, Reichenbach, and Popper) and metamethodology (Lakatos and Laudan). The correct response, I suggest, is to adopt an “evolutionary perspective.” This perspective is defended against one recent critic (Putnam). To argue the plausibility of a naturalistic approach, I next sketch a naturalistic account of theories and of theory choice. This account is then illustrated by the recent revolution in geology. In conclusion I return to Kuhn's question about the role of history in developing a naturalistic theory of science.

Quantitative laws are more typical of science than are generalizations involving observational predicates, yet much discussion of scientific inference takes the confirmation of a universal generalization by its instances to be typical and paradigmatic. The important difference is that measurement necessarily involves error. It is argued that because of error laws can no more be refuted by observation than they can be verified by observation. Without much background knowledge, tests of a law mainly provide evidence for the distribution of errors of measurement of the quantities involved. With more background knowledge, the data may contribute either to our knowledge of the error distributions, or to the grounds we have for accepting or rejecting the law. With enough background knowledge, data may verify as well as refute laws.

In this paper I expound an argument which seems to establish that probabilism and special relativity are incompatible. I examine the argument critically, and consider its implications for interpretative problems of quantum theory, and for theoretical physics as a whole.

The traditional views of science as the possessor of a special method, and as the epitome or apex of rationality, have come under severe challenges for a variety of historical, psychological, sociological, political, and philosophical reasons. As a result, many philosophers are either denying science its claim to rationality, or else casting about for a new account of its rationality. In this paper a defense of the traditional view is offered. It is argued that contemporary philosophical discussion regarding the rationality of science is plagued by a failure to distinguish among three different questions, all taken to be “the“ question of the rationality of science. Once these questions are delineated, it becomes possible to answer one of them in such a way that the traditional link between science's rationality and its method is reestablished–-although the scientific method is itself given a non-traditional rendering. In short, it is argued that there is a feature of science which is appropriately characterized as its method; that this method does in fact secure science's rationality; and that science is therefore correctly construed as preeminently rational. It is suggested in addition that the philosophy of science is itself best seen as a primarily epistemological activity, and consequently that a correction from the excessively historicist conception of recent philosophy of science is in order.

The aim of this paper is to develop an adequate version of the D-N theory of explanation for particular events and to show how the resulting D-N model can be used as a tool in articulating a regularity theory of causation and an analysis of the truth conditions for counterfactual conditionals. Starting with a basic model that is largely the product of other workers in this field, two new restrictions are formulated in order to construct a version of D-N explanation that does not yield the counterintuitive results that have plagued all earlier versions. An additional condition is then developed that is indispensable for utilizing the D-N model as a tool in formulating a regularity theory of causation. Finally, it is shown how a suitable model of potential D-N explanation facilitates the formulation of a theory of counterfactual conditionals.

The main argument of this paper is that philosophical difficulties regarding scientific discovery arise mainly because philosophers base their arguments on a flawed picture of scientific research. Careful examination of N. R. Hanson's treatment of Kepler's discovery not only puts the rationality of this discovery beyond question, it also reveals what its rationality consists in. We can retrieve the point stressed by Hanson concerning the rational character of discoveries such as Kepler's even as we reject the type of “logical” analysis he proposes.

Einstein's papers on relativity, quantum theory, and statistical mechanics were all part of a single research program; the aim was to unify mechanics and electrodynamics. It was this broader program–which eventually split into relativistic physics and quantum mechanics–that superseded Lorentz's theory. The argument of this paper is partly historical and partly methodological. A notion of “crossbred objects“–theoretical objects with contradictory properties which are part of the domain of application of two different research programs–is developed that explains the dynamics of revolutionary theory change.

Arthur Fine's use of prism models to provide local and deterministic accounts of quantum correlation experiments is presented and analyzed in some detail. Fine's claim that “there is … no question of the consistency of prism models … with the quantum theory“ (forthcoming, p. 16) is disputed. Our criticisms are threefold: (1) consideration of the possibility of additional analyzer positions shows that prism models entail unacceptably high rejection rates in the relevant experiments; (2) similar considerations show that the models are at best only superficially local and deterministic; and (3) in any case, Fine extracts the quantum correlation statistics from prism models only by resurrecting conceptual problems similar to those that his models were to designed to solve.

Bayesians often confuse insistence that probability judgment ought to be indeterminate (which is incompatible with Bayesian ideals) with recognition of the presence of imprecision in the determination or measurement of personal probabilities (which is compatible with these ideals). The confusion is discussed and illustrated by remarks in a recent essay by R. C. Jeffrey.

By a logical theory I mean a formal system together with its semantics, meta-theory, and rules for translating ordinary language into its notation. Logical theories can be used descriptively (for example, to represent particular arguments or to depict the logical form of certain sentences). Here the logician uses the usual methods of empirical science to assess the correctness of his descriptions. However, the most important applications of logical theories are normative, and here, I argue, the epistemology is that of wide reflective equilibrium. The result is that logic not only assesses our inferential practice but also changes it. I tie my discussion to Thagard's views concerning the relationship between psychology and logic, arguing against him that psychology has and should have only a peripheral role in normative (and most descriptive) applications of logic.

The ambiguity to which Porpora (1980) objects in Wright's (1972, 1976) analysis of goal-directedness permits certain counterexamples to Porpora's analysis to be easily accommodated by Wright's. As a consequence, Ringen's (1976) claim that some operant behavior is goal-directed is in accord with Wright's analysis and with certain features of common sense that Wright's analysis captures. However, the way our commonsense conception of goal-directedness accommodates some of the counterexamples to Porpora's analysis suggests an intimate connection between goal-directedness and intentional notions like belief and desire. This suggests a possible criticism of Ringen and highlights problematic aspects of contemporary folk psychology.

The paper attempts to elucidate and evaluate William Whewell's notion of a “consilience of inductions.“ In section I Whewellian consilience is defined and shown to differ considerably from what latter-day writers talk about when they use the term. In section II a primary analysis of consilience is shown to yield two types of consilient processes, one in which one of the lower-level laws undergoes a conceptual change (the case aptly discussed in Butts [1977]), and one in which the explanatory theory undergoes conceptual “stretching.” In section III both consilient cases are compared to the non-consilient case in reference to L. J. Cohen's method of relevant variables. In section IV we examine the test procedures of the theory in all three cases, and it is shown that in the event of genuine consilience (consilience of the second type) a theory acquires extraordinarily high support. In the final section something is said of the shortcomings of standard Bayesian confirmation theories that are highlighted by Whewellian consilience.

Exploratory statistics represents the transformation of a realist theory of statistics held by early nineteenth-century astronomers into an empiricist theory of statistics held by biometricians at the turn of the twentieth century. This paper discusses four key ideas in empiricist thought that influenced the form exploratory statistics took: (1) Baconianism, (2) associationism, (3) the search for cognitive calculi, and (4) phenomenalism.

Some limitations of and alternatives to exploratory statistics as a hypothesis-generating methodology are discussed.

Uncertainty relations and complementarity of canonically conjugate position and momentum observables in quantum theory are discussed with respect to some general coupling properties of a function and its Fourier transform. The question of joint localization of a particle on bounded position and momentum value sets and the relevance of this question to the interpretation of position-momentum uncertainty relations is surveyed. In particular, it is argued that the Heisenberg interpretation of the uncertainty relations can consistently be carried through in a natural extension of the usual Hilbert space frame of the quantum theory.

In clinical and agricultural trials, there is the danger that an experimental outcome appears to arise from the causal process or treatment one is interested in when, in reality, it was produced by some extraneous variation in the experimental conditions. The remedy prescribed by classical statisticians involves the procedure of randomization, whose effectiveness and appropriateness is criticized. An alternative, Bayesian analysis of experimental design, is shown, on the other hand, to provide a coherent and intuitively satisfactory solution to the problem.

Three recent, influential critiques (Stich 1978; Fodor 1981c; Block 1980) have argued that various tasks on the agenda for computational psychology put conflicting pressures on its theoretical constructs. Unless something is done, the inevitable result will be confusion or outright incoherence. Stich, Fodor, and Block present different versions of this worry and each proposes a different remedy. Stich wants the central notion of belief to be jettisoned if it cannot be shown to be sound. Fodor tries to reduce confusion in computational psychology by dismissing some putative tasks as impossible. Block argues that the widespread faith in functionalism is just not warranted. I argue that all these critiques are misguided because they depend on holding cognitive psychology to taxonomic standards that other sciences routinely rise above.

Should we act only for the sake of what we might bring about (causal decision theory); or is it enough for a decent motive that our action is highly correlated with something desirable (evidential decision theory)? The conflict between these points of view is embodied in Newcomb's problem. It is argued here that intuitive evidence from familiar decision contexts does not enable us to settle the issue, since the two theories dictate the same results in normal circumstances. Nevertheless, there are several reasons to reject the causal approach: (1) its relative complexity; (2) its commitment to the existence of situations in which every possible act would be irrational; (3) its incorporation of an arbitrary time bias; and (4) its implicit distinction between what ought to be done and what ought to be hoped for.

This paper shows that species are individuals with respect to evolutionary theory in the sense that the laws of the theory deal with species as irreducible wholes rather than as sets of organisms. ‘Species X’ is an instantiation of a primitive term of the theory. I present a sketch of a proof that it cannot be defined within the theory as a set of organisms; the proof relies not on details of my axiomatization but rather on a generally accepted property of speciation; hence the same argument should work for any axiomatization which captures this generally accepted property of speciation.