Predictivism holds that, where evidence E confirms theory T, E confirms T more strongly when E is predicted on the basis of T and subsequently confirmed than when E is known in advance of T's formulation and used, in some sense, in the formulation of T. Predictivism has lately enjoyed some strong supporting arguments from Maher (1988, 1990, 1993) and Kahn, Landsberg, and Stockman (1992). Despite the many virtues of the analyses these authors provide it is my view that they (...) (along with all other authors on this subject) have failed to understand a fundamental truth about predictivism: the existence of a scientist who predicted T prior to the establishment that E is true has epistemic import for T (once E is established) only in connection with information regarding the social milieu in which the T-predictor is located and information regarding how the T-predictor was located. The aim of this paper is to show that predictivism is ultimately a social phenomenon that requires a social level of analysis, a thesis I deem social predictivism. (shrink)

I develop an account of predictive similarity that allows even Antirealists who accept a correspondence conception of truth to answer the Realist demand (recently given sophisticated reformulations by Musgrave and Leplin) to explain the success of particular scientific theories by appeal to some intrinsic feature of those theories (notwithstanding the failure of past efforts by van Fraassen, Fine, and Laudan). I conclude by arguing that we have no reason to find truth a better (i.e., more plausible) explanation of a theory's (...) success than predictive similarity, even of its success in making novel predictions. (shrink)

While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that (...) novelty matters is the deeper intuition that severe tests matter. I set out a criterion of severity akin to the notion of a test's power in Neyman-Pearson statistics. I argue that tests which are use-novel may fail to be severe, and tests that are severe may fail to be use-novel. I discuss the 1919 eclipse data as a severe test of Einstein's law of gravity. (shrink)

Evidence that confirms a scientific hypothesis is said to be ‘novel’ if it is not discovered until after the hypothesis isconstructed. The philosophical issues surrounding novel confirmation have been well summarized by Campbell and Vinci [1983]. They write that philosophers of science generally agree that when observational evidence supports a theory, the confirmation is much stronger when the evidence is ‘novel’... There are, nevertheless, reasons to be skeptical of this tradition... The notion of novel confirmation is beset with a theoretical (...) puzzle about how the degree of confirmation can change without any change in the evidence, hypothesis, or auxiliary assumptions... There have not yet appeared any obviously satisfactory solutions to these problems Much of the literature on novel confirmation relies on the Bayesian analysis of conditional probabilities. Let H represent a hypothesis, E an event that confirms the hypothesis, and B some relevant background information. Denote by Pr the conditional probability of x given y. There are various plausible measures of the degree of support that E lends to H. Among these are: Degree of support = Pr Degree of support = Pr – Pr Degree of support = Pr – Pr Degree of support = Pr – Pt In the work cited, Campbell and Vinci offer a somewhat more involved Bayesian interpretation. Formula is discussed by Gardner [1981] who points out that under this formulation there can be no role for novelty. 'The function Pr contains no third slot in which to insert a temporal relation between the invention of H and the inventor's learning E. Obviously, then, this relation could not possibly affect E's support of H.' Formula, on the other hand, suggests a role for novelty. Bayes's Theorem allows us to rewrite the formula as Degree of support = Pr x [Pr – 1] We can use 1/Pr as a measure of the novelty of E. Then shows that the degree of support increases with novelty of E. In the paper already cited, Campbell and Vinci discuss shortcomings of this analysis. Formula expresses an alternative offered by Howson [1984]. In that formula, Pr represents the probability of H assuming that only B – {E} is known. This allows for the possibility of non-novel facts generating support for hypotheses. Niiniluoto [1984] argues for a variant along the lines of, in which we account for the possibility that the theorist was unaware that his hypothesis entails E. One problem in deciding among these approaches is that the choice of a definition for the degree of support appears arbitrary. What kind of argument could justify the choice of one definition over another? It is our position that there can be no basis for addressing this question in the absence of an explicit model of the process by which hypotheses are generated. Only in the presence of such a model can the various conditional probabilities be given meaningful interpretations. We provide such models in Sections 1, 2 and 4. The simple model of Section 1, incorporating strong assumptions, yields the conclusion that novelty is irrelevant. When these assumptions are relaxed in the later sections, novelty becomes relevant for a variety of reasons. It is at least potentially the case that scientists have more information about their own abilities than is publicly available, and this information might influence their decisions about whether even to attempt novel prediction. If this is so, then it should be incorporated into the model of hypothesis generation. This requires an explicit discussion of how scientists respond to incentives and how the incentives themselves evolve, which in turn takes us into the realm of economic theory. We have addressed these issues in another paper, written for an audience of economists. The results of this research are summarized in Section 3. (shrink)

Maher (1988, 1990) has recently argued that the way a hypothesis is generated can affect its confirmation by the available evidence, and that Bayesian confirmation theory can explain this. In particular, he argues that evidence known at the time a theory was proposed does not confirm the theory as much as it would had that evidence been discovered after the theory was proposed. We examine Maher's arguments for this "predictivist" position and conclude that they do not, in fact, support his (...) view. We also cast doubt on the assumptions of Maher's alleged Bayesian proofs. (shrink)

A widely endorsed thesis in the philosophy of science holds that if evidence for a hypothesis was not known when the hypothesis was proposed, then that evidence confirms the hypothesis more strongly than would otherwise be the case. The thesis has been thought to be inconsistent with Bayesian confirmation theory, but the arguments offered for that view are fallacious. This paper shows how the special value of prediction can in fact be given Bayesian explanation. The explanation involves consideration of the (...) reliability of the method by which the hypothesis was discovered, and thus reveals an intimate connection between the 'logic of discovery' and confirmation theory. (shrink)

This paper examines the famous doctrine that independent prediction garners more support than accommodation. The standard arguments for the doctrine are found to be invalid, and a more realistic position is put forward, that whether evidence supports or not a hypothesis depends on the prior probability of the hypothesis, and is independent of whether it was proposed before or after the evidence. This position is implicit in the subjective Bayesian theory of confirmation, and the paper ends with a brief account (...) of this theory, and answer to the principal objections to it. (shrink)

Evidence for a hypothesis typically confirms the hypothesis more if the evidence was predicted than if it was accommodated. Or so I argued in previous papers, where I also developed an analysis of why this should be so. But this was all a mistake if Howson and Franklin (1991) are to be believed. In this paper, I show why they are not to be believed. I also identify a grain of truth that may have been dimly grasped by those Bayesians (...) who deny the confirmatory value of prediction. (shrink)

The realist programme has degenerated by now to the point where it is quite beyond salvage. A token of this degeneration is that there are altogether too many realisms. It is as though by splitting into a confusing array of types and kinds, realism has hoped that some one variety might yet escape extinct. I shall survey the debate, and some of these realisms, below. Here I would just point out the obvious; that in so far as the successes of (...) science mount while realism continues to decline we must conclude that scientific success lends no support to realism. Since it is unlikely to support anti-realism, we have some reason to suspect that the philosophical debate over realism does not concern issues that can be settled by developments in the sciences, no matter how successful science may be. Further, since that success grounds a culture of acceptance for science and its entities, we have reason to believe that the existence of those entities is also not actually the issue that concerns realism. Afortiori, it is not the issue that concerns anti-realism either; nor, I might add, is anti-realism the winner in the philosophical debate that realism has lost. (shrink)