Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy of the counterexample and of (...) some previous responses to it, as well as illustrating more appropriate measures of probabilistic dependence in the nonstationary case. A challenge to the principle of the common causeSober's argument and the attempts to rescue the principleProbabilistic dependenceNonstationary time seriesProbabilistic dependence in nonstationary time seriesDo Venetian sea levels and British bread prices violate the principle of the common cause? (shrink)

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, (...) we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction. (shrink)

Thought experiment acquires evidential significance only on particular metaphysical assumptions. These include the thesis that science aims at uncovering "phenomena"universal and stable modes in which the world is articulatedand the thesis that phenomena are revealed imperfectly in actual occurrences. Only on these Platonically inspired assumptions does it make sense to bypass experience of actual occurrences and perform thought experiments. These assumptions are taken to hold in classical physics and other disciplines, but not in sciences that emphasize variety and contingency, such (...) as Aristotelian natural philosophy and some forms of historiography. This explains why thought experiments carry weight in the former but not the latter disciplines. (shrink)

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

An a priori thesis about evidence, defended by many, states that the only empirical fact that can affect the truth of an objective evidence claim of the form ‘e is evidence for h’ (or ‘e confirms h to degree r’) is the truth of e; all other considerations are a priori. By examining cases involving evidential flaws, I challange this claim and defend an empirical concept of evidence. In accordance with such a concept, whether, and the extent to which, e, (...) if true, confirms h is an empirical, not a priori, fact. (shrink)

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.

The Description for this book, Theory and Evidence, will be forthcoming.

Probability is a guide to life partly because it is a guide to causality. Work over the last two decades using Bayes nets supposes that probability is a very sure guide to causality. I think not, and I shall argue that here. Almost all the objections I list are well-known. But I have come to see them in a different light by reflecting again on the original work in this area by Wolfgang Spohn and his recent defense of it in (...) a paper titled “Bayesian Nets Are All There Is to Causality”. (shrink)

This is a study of the logic of the concept of evidence. Two distinct concepts of evidence will be explicated and analyzed: confirming evidence by means of which an hypothesis is established, and supporting evidence which does not establish the hypothesis, but merely renders it more tenable. The formal characteristics of each of these concepts of evidence will be examined in detail in Part II. In Part III these considerations are used as a basis for a survey of rules of (...) evidence in order to establish the systematic character of the evidential relationship from the standpoint of formal logic. Finally, the Conclusion appends a brief indication of the methodological bearing of the logical theory of evidence. (shrink)