Bootstrapping is a suspicious form of reasoning that verifies a source's reliability by checking it against itself. Theories that endorse such reasoning face the bootstrapping problem. This article considers which theories face the problem, and surveys potential solutions. The initial focus is on theories like reliabilism and dogmatism, which allow one to gain knowledge from a source without knowing that it is reliable. But the discussion quickly turns to a more general version of the problem that does not depend on (...) this allowance. Five potential solutions to the general problem are evaluated, and some implications for the literature on peer disagreement are considered. (shrink)

I argue that a criterion of theoretical equivalence due to Glymour :227–251, 1977) does not capture an important sense in which two theories may be equivalent. I then motivate and state an alternative criterion that does capture the sense of equivalence I have in mind. The principal claim of the paper is that relative to this second criterion, the answer to the question posed in the title is “yes”, at least on one natural understanding of Newtonian gravitation.

In a recent paper James Bogen and James Woodward denounce a set of views on confirmation that they collectively brand 'IRS'. The supporters of these views cast confirmation in terms of Inferential Relations between observational and theoretical Sentences. Against 1RS accounts of confirmation, Bogen and Woodward unveil two main objections: (a) inferential relations are not necessary to model confirmation relations since many data are neither in sentential form nor can they be put in such a form and (b) inferential relations (...) are not sufficient to model confirmation relations because the former cannot capture evidentially relevant factors about the detection processes and instruments that generate the data. In this paper I have a two-fold aim: (i) to show that Bogen and Woodward fail to provide compelling grounds for the rejection of IRS models and (ii) to highlight some of the models' neglected merits. (shrink)

The story of how Perrin’s experimental work established the reality of atoms and molecules has been a staple in (realist) philosophy of science writings (Wesley Salmon, Clark Glymour, Peter Achinstein, Penelope Maddy, …). I’ll argue that how this story is told distorts both what the work was and its significance, and draw morals for the understanding of how theories can be or fail to be empirically grounded.

Logic and philosophy of science share a long history, though contacts have gone through ups and downs. This paper is a brief survey of some major themes in logical studies of empirical theories, including links to computer science and current studies of rational agency. The survey has no new results: we just try to make some things into common knowledge.

The frequent occurrence of comorbidity has brought about an extensive theoretical debate in psychiatry. Why are the rates of psychiatric comorbidity so high and what are their implications for the ontological and epistemological status of comorbid psychiatric diseases? Current explanations focus either on classification choices or on causal ties between disorders. Based on empirical and philosophical arguments, we propose a conventionalist interpretation of psychiatric comorbidity instead. We argue that a conventionalist approach fits well with research and clinical practice and resolves (...) two problems for psychiatric diseases: experimenter’s regress and arbitrariness. (shrink)

Much of scientific inference involves fitting numerical data with a curve, or functional relation. The received view is that the fittest curve is the curve which best balances the conflicting demands of simplicity and accuracy, where simplicity is measured by the number ofparameters in the curve. The problem with this view is that there is no commonly accepted justification for desiring simplicity. This paper presents a measure of the stability of equations. It is argued that the fittest curve is the (...) curve which best balances stability and accuracy. The received view is defended with a proof that simplicity corresponds to stability, for linear regression equations. 1This paper is based on part of my doctoral dissertation. My thanks go to my thesis supervisor Professor Alasdair Urquhart for his encouragement, constructive criticism, and for directing me to several relevant articles: to my advisor Professor Ian Hacking for reminding me to concentrate on results that might have some application in the real world; and to my friend Wendy Brandts for sharing her ideas on a closely related problem. My thanks also to an anonymous referee of The British Journal of the Philosophy of Science for several helpful comments, to my friends and family for unfailing support, and to the Social Sciences and Humanities Research Council (awards 452-86-5885 and 453-87-0513) and the University of Toronto for financial assistance. (shrink)

Philosophers have often noted that science displays an uncommon degree of consensus on beliefs among its practitioners. Yet consensus in the sciences is not a goal in itself. I consider cases of consensus on beliefs as concrete events. Consensus on beliefs is neither a sufficient nor a necessary condition for presuming that these beliefs constitute knowledge. A concrete consensus on a set of beliefs by a group of people at a given historical period may be explained by different factors according (...) to various hypotheses. A particularly interesting hypothesis from an epistemic perspective is the knowledge hypothesis: shared knowledge explains a consensus on beliefs. If all the alternative hypotheses to the knowledge hypotheses are false or are not as good in explaining a concrete consensus on beliefs, the knowledge hypothesis is the best explanation of the consensus. If the knowledge hypothesis is best, a consensus becomes a plausible, though fallible, indicator of knowledge. I argue that if a consensus on beliefs is uncoerced, uniquely heterogeneous and large, the gap between the likelihood of the consensus given the knowledge hypothesis and its likelihoods given competing hypotheses tends to increase significantly. Consensus is a better indicator of knowledge than "success" or "human flourishing". (shrink)

RÉSUMÉ: La pragmatique et la sémantique de l’historiographie révèlent une fragmentation croissante qui s’étend par-delà les écoles jusqu’aux historiens individuels. Alors que les scientifiques normalisent les données pour qu’elles s’ajustent aux théories, les historiens interprètent leurs théories, de manières incompatibles entre elles, pour qu’elles s’ajustent aux différents cas historiques. Les difficultés qui en découlent dans la communication historiographique remettent en cause les philosophies herméneutiques de l’historiographie et redonnent un nouvel intérêt à la question d’une historiographie scientifique. Mais les réponses existantes (...) sont philosophiquement obsolètes. Une façon de reformuler le problème est de partir de la complexité du chaos et de l’unicité de l’histoire. Seule la science peut évaluer si les propriétés d’un domaine donné en interdisent une approche scientifique. Or la science s’étend par réductions méthodologiques explicatives, en ramenant des propriétés d’événements complexes, chaotiques et uniques, qui sont familières, mais d’un niveau plus élevé, à des propriétés ou interrelations non familières et d’un niveau moins élevé. La culture disciplinaire de l’historiographie, cependant, empêche le développement par essais et erreurs de telles tentatives de réductions scientifiques, qui seules permettraient d’évaluer les possibilités d’une historiographie scientifique, laquelle, dans la situation présente, relève de la science-fiction. (shrink)

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

The author uses a series of examples to illustrate two versions of a new, nonprobabilist principle of epistemic rationality, the special and general versions of the metacognitive, expected relative frequency principle. These are used to explain the rationality of revisions to an agent’s degrees of confidence in propositions based on evidence of the reliability or unreliability of the cognitive processes responsible for them—especially reductions in confidence assignments to propositions antecedently regarded as certain—including certainty-reductions to instances of the law of excluded (...) middle or the law of noncontradiction in logic or certainty-reductions to the certainties of probabilist epistemology. The author proposes special and general versions of the MERF principle and uses them to explain the examples, including the reasoning that would lead to thoroughgoing fallibilism—that is, to a state of being certain of nothing. The author responds to the main defenses of probabilism: Dutch Book arguments, Joyce’s potential accuracy defense, and the potential calibration defenses of Shimony and van Fraassen by showing that, even though they do not satisfy the probability axioms, degrees of belief that satisfy the MERF principle minimize expected inaccuracy in Joyce’s sense; they can be externally calibrated in Shimony and van Fraassen’s sense; and they can serve as a basis for rational betting, unlike probabilist degrees of belief, which, in many cases, human beings have no rational way of ascertaining. The author also uses the MERF principle to subsume the various epistemic akrasia principles in the literature. Finally, the author responds to Titelbaum’s argument that epistemic akrasia principles require that we be certain of some epistemological beliefs, if we are rational. (shrink)

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful”. The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not produce any additional predictions, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying terms of this kind that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning: I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

Alexander Rosenberg has played a large role in creating the philosophy of economics as a distinct area of philosophy. But since the publication of Microeconomic Laws in 1976, Professor Rosenberg's thinking about economics has been casting the subject in an increasingly uncomplimentary light. This development is reflected in Rosenberg's new book Economics–Mathematical Politics or Science of Diminishing Returns? In this stimulating work Rosenberg endorses the view that economics does not constitute scientific empirical knowledge. He says.

Amalgamating evidence of different kinds for the same hypothesis into an overall confirmation is analogous, I argue, to amalgamating individuals’ preferences into a group preference. The latter faces well-known impossibility theorems, most famously “Arrow’s Theorem”. Once the analogy between amalgamating evidence and amalgamating preferences is tight, it is obvious that amalgamating evidence might face a theorem similar to Arrow’s. I prove that this is so, and end by discussing the plausibility of the axioms required for the theorem.

Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...) more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science. (shrink)

Hypothetico-deductive (H-D) confirmation builds on the idea that confirming evidence consists of successful predictions that deductively follow from the hypothesis under test. This article reviews scope, history and recent development of the venerable H-D account: First, we motivate the approach and clarify its relationship to Bayesian confirmation theory. Second, we explain and discuss the tacking paradoxes which exploit the fact that H-D confirmation gives no account of evidential relevance. Third, we review several recent proposals that aim at a sounder and (...) more comprehensive formulation of H-D confirmation. Finally, we conclude that the reputation of hypothetico-deductive confirmation as outdated and hopeless is undeserved: not only can the technical problems be addressed satisfactorily, the hypothetico-deductive method is also highly relevant for scientific practice. (shrink)

Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...) reasoning with idealized models in science. (shrink)

One of the most troubling and persistent challenges for Bayesian Confirmation Theory is the Problem of Old Evidence. The problem arises for anyone who models scientific reasoning by means of Bayesian Conditionalization. This article addresses the problem as follows: First, I clarify the nature and varieties of the POE and analyze various solution proposals in the literature. Second, I present a novel solution that combines previous attempts while making weaker and more plausible assumptions. Third and last, I summarize my findings (...) and put them into the context of the general debate about POE and Bayesian reasoning. (shrink)

This paper synthesizes confirmation by instances and confirmation by successful predictions, and thereby the Hempelian and the hypothetico-deductive traditions in confirmation theory. The merger of these two approaches is subsequently extended to the piecemeal confirmation of entire theories. It is then argued that this synthetic account makes a useful contribution from both a historical and a systematic perspective.

The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the (...) primary criterion for ‘best', the mathematical approximation perspective undermines the reliability of inference objective by giving rise to selection rules which pay insufficient attention to ‘accounting for the regularities in the data'. A more appropriate framework is offered by the error-statistical approach, where (i) statistical adequacy provides the criterion for assessing when a curve captures the regularities in the data adequately, and (ii) the relevant error probabilities can be used to assess the reliability of inductive inference. Broadly speaking, the fittest curve (statistically adequate) is not determined by the smallness if its residuals, tempered by simplicity or other pragmatic criteria, but by the nonsystematic (e.g. white noise) nature of its residuals. The advocated error-statistical arguments are illustrated by comparing the Kepler and Ptolemaic models on empirical grounds. ‡I am grateful to Deborah Mayo and Clark Glymour for many valuable suggestions and comments on an earlier draft of the paper; estimating the Ptolemaic model was the result of Glymour's prompting and encouragement. †To contact the author, please write to: Department of Economics, Virginia Tech 3019 Pamplin Hall (0316), Blacksburg, VA 24061; e-mail: [email protected]. (shrink)

The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...) the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign ‘single event’ probabilities, and [iii] its reliance on ‘random samples’. It is argued that charges [i]–[ii] stem from misidentifying the frequentist ‘long-run’ with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. (shrink)

This paper defends reductionism about testimonial justification of beliefs against two influential arguments. One is the empirical argument to the effect that the reductionist justification of our trust in testimony is either circular since it relies on testimonial evidence or else there is scarce evidence in support of our trust in testimony. The other is the transcendental argument to the effect that trust in testimony is a prerequisite for the very existence of testimonial evidence since without the presumption of people’s (...) truthfulness we cannot interpret their utterances as testimony with propositional contents. This paper contends that the epistemic subject can interpret utterances as testimony with propositional contents without presupposing the credibility of testimony, and that evidence available to the normal epistemic subject can justify her trust in testimony. (shrink)

Reichenbach's Philosophy of Space and Time (1928) avoids most of the logical positivist pitfalls it is generally held to exemplify, notably both conventionalism and verificationism. To see why, we must appreciate that Reichenbach's interest lies in how mathematical structures can be used to describe reality, not in how words like 'distance' acquire meaning. Examination of his proposed "coordinative definition" of congruence shows that Reichenbach advocates a reductionist analysis of the relations figuring in physical geometry (contrary to common readings that attribute (...) to him a holistic conventionalism), while embracing a thoroughly holistic understanding of empirical confirmation (contrary to rival operationalist readings). (shrink)

Tacking by conjunction is a well-known problem for Bayesian confirmation theory. In the first section, disadvantages of existing Bayesian solution proposals to this problem are pointed out and an alternative solution proposal is presented: that of genuine confirmation. In the second section, the notion of GC is briefly recapitulated and three versions of GC are distinguished: full GC, partial GC and quantitative GC. In the third section, the application of partial GC to pure post-facto speculations is explained. In the fourth (...) section it is demonstrated that full GC is a necessary condition for Bayesian convergence to certainty based on the accumulation of conditionally independent pieces of evidence. It is found that whenever a hypothesis is equivalent to a disjunction of more fine-grained hypotheses conveying different probabilities to the evidence, then conditional independence of the evidence fails. This failure occurs typically for unspecific negations of hypotheses. A refined version of the convergence to certainty theorem that overcomes this difficulty is developed in the final section. (shrink)

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...) literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification. (shrink)

Tacking by conjunction is a deep problem for Bayesian confirmation theory. It is based on the insight that to each hypothesis h that is confirmed by a piece of evidence e one can ‘tack’ an irrelevant hypothesis h′ so that h∧h′ is also confirmed by e. This seems counter-intuitive. Existing Bayesian solution proposals try to soften the negative impact of this result by showing that although h∧h′ is confirmed by e, it is so only to a lower degree. In this (...) article we outline some problems of these proposals and develop an alternative solution based on a new concept of confirmation that we call genuine confirmation. After pointing out that genuine confirmation is a necessary condition for cumulative confirmation we apply this notion to the tacking by conjunction problem. We consider both the question of what happens when irrelevant hypotheses are added to a hypothesis h that is confirmed by e as well as the question of what happens when h is disconfirmed. The upshot of our discussion will be that genuine confirmation provides a robust solution for each of the different perspectives. _1_ Introduction _2_ Tacking by Conjunction: Existing Solution Proposals _3_ Genuine Confirmation _3.1_ Content elements and content parts _3.2_ Qualitative genuine confirmation _3.3_ Quantitative genuine confirmation _4_ Tacking by Conjunction: The Case of Confirmation _5_ Tacking by Conjunction: The Case of Disconfirmation _6_ Tacking by Conjunction: Adding Multiple Hypotheses _7_ Conclusion Appendix. (shrink)

Intelligent Design creationism is often criticized for failing to be science because it falls afoul of some demarcation criterion between science and non-science. This paper argues that this objection to Intelligent Design is misplaced because it assumes that a consistent non-theological characterization of Intelligent Design is possible. In contrast, it argues that, if Intelligent Design is taken to be non-theological doctrine, it is not intelligible. Consequently, a demarcation criterion cannot be used to judge its status. This position has the added (...) advantage of providing reasons to reject Intelligent Design creationism without invoking potentially philosophically controversial demarcation criteria. (shrink)

Re-Engineering Philosophy for Limited Beings is about new approaches to many of the big topics in philosophy of science today, but with a very different take. To begin with, we are urged to reject the received Cartesian-Laplacean myths: Descartes’ certainty and Laplace’s computational omniscience. Instead, Wimsatt re-engineers a philosophy for human beings with all their cognitive limitations. His approaches find their starting point in the actual practices of scientists themselves, which he strongly identifies with engineering practices as the source of (...) researchers’ solutions for dealing with a complex world. He aims to construct an understanding of scientific methodology around the central role of reduction. But he dismisses eliminative reductionism in favor of a heuristic-based realist view. Wimsatt’s world is a complex one, and this means that science needs to do away with all the absolute and simple answers, because they do not reflect the world we are living in. A complex world requires the mindset and tinkering of an engineer to uncover its reality. The appropriate response must be heuristics all the way down as we constantly seek out reliable inferences on often shifting ground. To this end, we aim for models and theories that are robust, just as engineers aim to build robust machines. And although errors occur and approaches are fallible, they allow us to continually adapt the heuristics applied and sharpen our perceptions so as to develop more refined tools for investigating and understanding the world. (shrink)

The paper investigates the epistemological and communicative competences the experts need to use and communicate evidence in the reasoning process leading to diagnosis. The diagnosis and diagnosis communication are presented as intertwined processes that should be jointly addressed in medical consultations, to empower patients’ compliance in illness management. The paper presents defeasible reasoning as specific to the diagnostic praxis, showing how this type of reasoning threatens effective diagnosis communication and entails that we should understand diagnostic evidence as defeasible as well. (...) It argues that metaphors might be effective communicative devices to let the patients understand the relevant defeasors in the diagnostic reasoning process, helping to improve effective diagnosis communication, and also encouraging a change in patients’ beliefs and attitudes on their own experience of illness and illness’ management. (shrink)

In this paper, we examine the relationship between general relativity and the theory of Einstein algebras. We show that according to a formal criterion for theoretical equivalence recently proposed by Halvorson and Weatherall, the two are equivalent theories.