There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential contexts , we (...) can see that neither weak nor strong predictivism captures all of the reasons for valuing prediction available. A third path is presented, Pluralist Instrumental Predictivism; PIP for short. (shrink)

This classic work in the philosophy of physical science is an incisive and readable account of the scientific method. Pierre Duhem was one of the great figures in French science, a devoted teacher, and a distinguished scholar of the history and philosophy of science. This book represents his most mature thought on a wide range of topics.

The history of biochemistry and molecular biology is replete with examples of erroneous theories that persisted for considerable lengths of time before they were rejected. This paper examines patterns of dissolution of three such erroneous hypotheses: The idea that nucleic acids are tetrads of the four nucleobases (‘the tetranucleotide hypothesis’); the notion that proteins are collinear with their encoding genes in all branches of life; and the hypothesis that proteins are synthesized by reverse action of proteolytic enzymes. Analysis of these (...) cases indicates that amassed contradictory empirical findings did not prompt critical experimental testing of the prevailing theories nor did they elicit alternative hypotheses. Rather, the incorrect models collapsed when experiments that were not purposely designed to test their validity exposed new facts. (shrink)

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of them, the (...) key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments. (shrink)

According to the thesis of Strong Predictionism, we typically have stronger evidence for a theory if it was used to predict certain data, than if it was deliberately constructed to accommodate those same data, even if we fully grasp the theory and all the evidence on which it was based. This thesis faces powerful objections and the existing arguments in support of it are seriously flawed. I offer a new defence of Strong Predictionism which overcomes the objections and provides a (...) deeper understanding of the epistemic importance of prediction. I conclude by applying this account to strategies for defending scientific realism. (shrink)

In this paper, I motivate the view that quantitative parsimony is a theoretical virtue: that is, we should be concerned not only to minimize the number of kinds of entities postulated by our theories (i. e. maximize qualitative parsimony), but we should also minimize the number of entities postulated which fall under those kinds. In order to motivate this view, I consider two cases from the history of science: the postulation of the neutrino and the proposal of Avogadro's hypothesis. I (...) also consider two issues concerning how a principle of quantitative parsimony should be framed. (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)

: In this paper I argue that belief in the greater confirmatory value of prediction over accommodation can best be understood as a function of the practice rather than the logic of science. Attempts to account for this asymmetry within the logic of science have revealed no non-arbitrary way to address the problem of underdetermination as it applies to prediction and thus have failed to account for the preference for prediction over accommodation on logical grounds. Instead, I propose a model (...) that not only justifies and explains this preference, but allows for a richer taxonomy of the types of evidential confirmation that are employed in scientific reasoning. (shrink)

According to a standard view, predictions of new phenomena provide stronger evidence for a theory than explanations of old ones. More guardedly, a theory that predicts phenomena that did not prompt the initial formulation of that theory is better supported by those phenomena than is a theory by known phenomena that generated the theory in the first place. So say various philosophers of science, including William Whewell (1847) in the 19th century and Karl Popper (1959) in the 20th, to mention (...) just two.Stephen Brush takes issue with this on historical grounds. In a series of fascinating papers he argues that generally speaking scientists do not regard the fact that a theory predicts new phenomena, even ones of a kind totally different from those that prompted the theory in the first place, as providing better evidential support for that theory than is provided by already known facts explained by the theory. (shrink)

It is widely believed that if a piece of evidence for a theory was known at the time the theory was proposed, then it does not confirm the theory as strongly as it would if the evidence had been discovered after the theory was proposed. I shall call this view the predictivist thesis. Those who have endorsed it include Leibniz (1678), Huygens (1690, preface), Whewell (1847 vol. 2, p. 64f.), Peirce (1883), Duhem (1914, ch. II, §5), Popper (1965, p. 241f.), (...) Lakatos (1970, p. 123), and Kuhn (1977, p. 322). On the other hand, the thesis has been rejected by a number of philosophers, including Mill (1872 bk. III, ch. 14, §6), Keynes (1921, p. 305), Rosenkrantz (1977, p. 169f.), Horwich (1982, pp. 108-117) and Schlesinger (1987). Others, while not rejecting the predictivist thesis, nevertheless regard the justification of the thesis as problematic; these include Hempel (1966, p. 38) and Gardner (1982). (shrink)

Going back at least to Duhem, there is a tradition of thinking that crucial experiments are impossible in science. I analyse Duhem's arguments and show that they are based on the excessively strong assumption that only deductive reasoning is permissible in experimental science. This opens the possibility that some principle of inductive inference could provide a sufficient reason for preferring one among a group of hypotheses on the basis of an appropriately controlled experiment. To be sure, there are analogues to (...) Duhem's problems that pertain to inductive inference. Using a famous experiment from the history of molecular biology as an example, I show that an experimentalist version of inference to the best explanation (IBE) does a better job in handling these problems than other accounts of scientific inference. Furthermore, I introduce a concept of experimental mechanism and show that it can guide inferences from data within an IBE-based framework for induction. 1. Introduction2. Duhem on the Logic of Crucial Experiments3. ‘The Most Beautiful Experiment in Biology’4. Why Not Simple Elimination?5. Severe Testing6. An Experimentalist Version of IBE 6.1. Physiological and experimental mechanisms6.2. Explaining the data6.3. IBE and the problem of untested auxiliaries6.4. IBE-turtles all the way down7. Van Fraassen's ‘Bad Lot’ Argument8. IBE and Bayesianism9. Conclusions. (shrink)

Reasoning in Biological Discoveries brings together a series of essays, which focus on one of the most heavily debated topics of scientific discovery. Collected together and richly illustrated, Darden's essays represent a groundbreaking foray into one of the major problems facing scientists and philosophers of science. Divided into three sections, the essays focus on broad themes, notably historical and philosophical issues at play in discussions of biological mechanism; and the problem of developing and refining reasoning strategies, including interfield relations and (...) anomaly resolution. Darden summarizes the philosophy of discovery and elaborates on the role that mechanisms play in biological discovery. Throughout the book, she uses historical case studies to extract advisory reasoning strategies for discovery. Examples in genetics, molecular biology, biochemistry, immunology, neuroscience and evolutionary biology reveal the process of discovery in action. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

Upon separation of the protein from the nucleic acid component of tobacco mosaic virus by phenol, using a fast and gentle procedure, the nucleic acid is infective in assays on tobacco leaves. A series of qualitative and quantitative control experiments demonstrates that the biological activity cannot depend on residual proteins in the preparation, but is a property of isolated nucleic acid which is thus the genetic material of the virus.

Ockham's razor, the principle of parsimony, states that simpler theories are better than theories that are more complex. It has a history dating back to Aristotle and it plays an important role in current physics, biology, and psychology. The razor also gets used outside of science - in everyday life and in philosophy. This book evaluates the principle and discusses its many applications. Fascinating examples from different domains provide a rich basis for contemplating the principle's promises and perils. It is (...) obvious that simpler theories are beautiful and easy to understand; the hard problem is to figure out why the simplicity of a theory should be relevant to saying what the world is like. In this book, the ABCs of probability theory are succinctly developed and put to work to describe two 'parsimony paradigms' within which this problem can be solved. (shrink)

In this powerful work of conceptual and analytical originality, the author argues for the primacy of the material arrangements of the laboratory in the dynamics of modern molecular biology. In a post-Kuhnian move away from the hegemony of theory, he develops a new epistemology of experimentation in which research is treated as a process for producing epistemic things. A central concern of the book is the basic question of how novelty is generated in the empirical sciences. In addressing this question, (...) the author brings French poststructuralist thinking—notably Jacques Derrida’s concepts of “différance” and “historiality”—to bear on the construction of epistemic things. Historiographical perspective shifts from the actors’ minds to their objects of manipulation. These epistemological and historical issues are illuminated in a detailed case study of a particular laboratory, that of the oncologist and biochemist Paul C. Zamecnik and his colleagues, located in a specific setting—the Collis P. Huntington Memorial Hospital of Harvard University at the Massachusetts General Hospital of Boston. The author traces how, between 1945 and 1965, this group developed an experimental system for synthesizing proteins in the test tube that put Zamecnik’s research team at the forefront of those who led biochemistry into the era of molecular biology. (shrink)

The desire to minimize the number of individual new entities postulated is often referred to as quantitative parsimony. Its influence on the default hypotheses formulated by scientists seems undeniable. I argue that there is a wide class of cases for which the preference for quantitatively parsimonious hypotheses is demonstrably rational. The justification, in a nutshell, is that such hypotheses have greater explanatory power than less parsimonious alternatives. My analysis is restricted to a class of cases I shall refer to as (...) additive. Such cases involve the postulation of a collection of qualitatively identical individual objects which collectively explain some particular observed phenomenon. Especially clear examples of this sort occur in particle physics. 1 Introduction 2 Particle physics: a case study 3 Three kinds of simplicity 4 Explanatory power 5 Explanation and non-observation 6 Parsimony and scientific methodology 7 Conclusions. (shrink)

Predictivism is the view that successful predictions of “novel” evidence carry more confirmational weight than accommodations of already known evidence. Novelty, in this context, has traditionally been conceived of as temporal novelty. However temporal predictivism has been criticized for lacking a rationale: why should the time order of theory and evidence matter? Instead, it has been proposed, novelty should be construed in terms of use-novelty, according to which evidence is novel if it was not used in the construction of a (...) theory. Only if evidence is use-novel can it fully support the theory entailing it. As I point out in this paper, the writings of the most influential proponent of use-novelty contain a weaker and a stronger version of use-novelty. However both versions, I argue, are problematic. With regard to the appraisal of Mendeleev’ periodic table, the most contentious historical case in the predictivism debate, I argue that temporal predictivism is indeed supported, although in ways not previously appreciated. On the basis of this case, I argue for a form of so-called symptomatic predictivism according to which temporally novel predictions carry more confirmational weight only insofar as they reveal the theory’s presumed coherence of facts as real. (shrink)

Ethics and jurisprudence are liable to the remark in common with logic. Almost every writer having taken a different view of some of the particulars which ...

The desire to minimize the number of individual new entities postulated is often referred to as quantitative parsimony. Its influence on the default hypotheses formulated by scientists seems undeniable. I argue that there is a wide class of cases for which the preference for quantitatively parsimonious hypotheses is demonstrably rational. The justification, in a nutshell, is that such hypotheses have greater explanatory power than less parsimonious alternatives. My analysis is restricted to a class of cases I shall refer to as (...) additive. Such cases involve the postulation of a collection of qualitatively identical individual objects which collectively explain some particular observed phenomenon. Especially clear examples of this sort occur in particle physics.1Introduction2Particle physics: a case study3Three kinds of simplicity4Explanatory power5Explanation and non‐observation6Parsimony and scientific methodology7Conclusions. (shrink)

ABSTRACT Our aim in this article is to offer a new justification for preferring theories that are more quantitatively parsimonious than their rivals. We discuss cases where it seems clear that those involved opted for more quantitatively parsimonious theories. We extend previous work on quantitative parsimony by offering an independent probabilistic justification for preferring the more quantitatively parsimonious theories in particular episodes of theory choice. Our strategy allows us to avoid worries that other considerations, such as pragmatic factors of computational (...) tractability and so on, could be the driving ones in the historical cases under consideration. _1_ Introduction _2_ Three Desiderata _2.1_ Limiting _2.2_ Robustness _2.3_ Breadth _2.3.1_ A limited success for Baker _2.3.2_ Rejecting Baker’s analysis _2.4_ The proposal _3_ Probabilistically Additive Hypotheses and a Bayesian Account: The Limpid Rationale Relativized and Reconsidered _3.1_ Neutrinos and beta decay _3.2_ Avogadro’s hypothesis _3.3_ Postulation of Neptune _4_ Conclusion. (shrink)

The paper presents a further articulation and defence of the view on prediction and accommodation that I have proposed earlier. It operates by analysing two accounts of the issue-by Patrick Maher and by Marc Lange-that, at least at first sight, appear to be rivals to my own. Maher claims that the time-order of theory and evidence may be important in terms of degree of confirmation, while that claim is explicitly denied in my account. I argue, however, that when his account (...) is analysed, Maher reveals no scientifically significant way in which the time-order counts, and that indeed his view is in the end best regarded as a less than optimally formulated version of my own. Lange has also responded to Maher by arguing that the apparent relevance of temporal considerations is merely apparent: what is really involved, according to Lange, is whether or not a hypothesis constitutes an "arbitrary conjunction." I argue that Lange's suggestion fails: the correct analysis of his and Maher's examples is that provided by my account. (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)

Are theories judged on the basis of empirical tests of their predictions, as proposed by Karl Popper and others, or are new theories adopted by younger scientists while old theories fade away when their advocates die, as Max Planck suggested? A famous historical episode, the rejection of steady state cosmology and the revival of the big bang cosmology following the 1965 discovery of the cosmic microwave background radiation, is examined to determine whether the scientific community followed Popper’s or Planck’s principle. (...) It is found that by 1975 almost all supporters of the steady state had either switched to the big bang or stopped publishing on cosmology. This case therefore seems to exemplify Popper’s principle, although it should be noted that two of the founders of steady state cosmology had strongly proclaimed their adherence to that principle. The case does not support the Popperian claim that successful novel predictions provide better evidence for a theory than deductions of known facts. (shrink)

ABSTRACT Rosalind Franklin is best known for her informative X-ray diffraction patterns of DNA that provided vital clues for James Watson and Francis Crick's double-stranded helical model. Her scientific career did not end when she left the DNA work at King's College, however. In 1953 Franklin moved to J. D. Bernal's crystallography laboratory at Birkbeck College, where she shifted her focus to the three-dimensional structure of viruses, obtaining diffraction patterns of Tobacco mosaic virus (TMV) of unprecedented detail and clarity. During (...) the next five years, while making significant headway on the structural determination of TMV, Franklin maintained an active correspondence with both Watson and Crick, who were also studying aspects of virus structure. Developments in TMV research during the 1950s illustrate the connections in the emerging field of molecular biology between structural studies of nucleic acids and of proteins and viruses. They also reveal how the protagonists of the “race for the double helix” continued to interact personally and professionally during the years when Watson and Crick's model for the double-helical structure of DNA was debated and confirmed. (shrink)

The thesis that scientists give greater weight to novel predictions than to explanations of known facts is tested against historical cases in physical science. Several theories were accepted after successful novel predictions but there is little evidence that extra credit was given for novelty. Other theories were rejected despite, or accepted without, making successful novel predictions. No examples were found of theories that were accepted primarily because of successful novel predictions and would not have been accepted if those facts had (...) been previously known. (shrink)

The debate about the relative epistemic weights carried in favour of a theory by predictions of new phenomena as opposed to accommodations of already known phenomena has a long history. We readdress the issue through a detailed re-examination of a particular historical case that has often been discussed in connection with it—that of Mendeleev and the prediction by his periodic law of the three ‘new’ elements, gallium, scandium and germanium. We find little support for the standard story that these predictive (...) successes were outstandingly important in the success of Mendeleev's scheme. Accommodations played an equal role—notably that of argon, the first of the ‘noble gases’ to be discovered; and the methodological situation in this chemical example turns out to be in interesting ways different from that in other cases—invariably from physics—that have been discussed in this connection. The historical episode when accurately analysed provides support for a different account of the relative weight of prediction and accommodation—one that is further articulated here. (shrink)

“What did the President know and when did he know it?”Senator Howard Baker, Watergate hearings, 1973Why do scientists accept or reject theories? More specifically: why do they change from one theory to another? What is the role of empirical tests in the evaluation of theories?This paper focuses on a narrowly-defined question: in judging theories, do scientists give greater weight (other things being equal) to successfulnovel predictionsthan to successful deductions of previously-known facts? The affirmative answer is called the “predictivist thesis” (Maher (...) 1988).It is primarily philosophers who are interested in this question, and they have treated it mostly as a normative or logical problem. Can the writings of historians of science tell us how scientists have treated novel predictions in the past? Until recently historians have rarely addressed this point. (shrink)

has offered a lovely example to motivate the intuition that a successful prediction has a kind of confirmatory significance that an accommodation lacks. This paper scrutinizes Maher's example. It argues that once the example is tweaked, the intuitive difference there between prediction and accommodation disappears. This suggests that the apparent superiority of prediction to accommodation is actually a side effect of an important difference between the hypotheses that tend to arise in each case.