There is not, of course, a one-to-one relation between policy conclusions and the conclusions of positive economics; if there were, there would be no ...

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. (...) In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

First published in 1949 expressly to introduce logical positivism to English speakers. Reichenbach, with Rudolph Carnap, founded logical positivism, a form of epistemofogy that privileged scientific over metaphysical truths.

The authors demonstrate that unselfish behavior is in fact an important feature of both biological and human nature. Their book provides a panoramic view of altruism throughout the animal kingdom--from self-sacrificing parasites to the human capacity for selflessness--even as it explains the evolutionary sense of such behavior.

When the New Organon appeared in 1620, part of a six-part programme of scientific inquiry entitled 'The Great Renewal of Learning', Francis Bacon was at the high point of his political career, and his ambitious work was groundbreaking in its attempt to give formal philosophical shape to a new and rapidly emerging experimentally-based science. Bacon combines theoretical scientific epistemology with examples from applied science, examining phenomena as various as magnetism, gravity, and the ebb and flow of the tides, and anticipating (...) later experimental work by Robert Boyle and others. His work challenges the entire edifice of the philosophy and learning of his time, and has left its mark on all subsequent philosophical discussions of scientific method. This volume presents a new translation of the text into modern English by Michael Silverthorne, and an introduction by Lisa Jardine that sets the work in the context of Bacon's scientific and philosophical activities. (shrink)

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

Reprint of the original, first published in 1869.

The problem of simplicity involves three questions: How is the simplicity of a hypothesis to be measured? How is the use of simplicity as a guide to hypothesis choice to be justified? And how is simplicity related to other desirable features of hypotheses -- that is, how is simplicity to be traded-off? The present paper explores these three questions, from a variety of viewpoints, including Bayesianism, likelihoodism, and the framework of predictive accuracy formulated by Akaike (1973). It may turn out (...) that simplicity has no global justification -- that its justification varies from problem to problem. (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.

First published in 2000. Routledge is an imprint of Taylor & Francis, an informa company.

Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships. For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that (...) aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science. Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments. (shrink)

In the days when the Great Central and the Midland ran competitive services from Leicester to London, I agreed to meet your train when you came up from Leicester. “It arrives at 12.5”, you said in your letter. But you neglected to mention which station, and only on the morning of your journey did each of us realize that this matter had been left open. You intended to travel from Leicester Central, it being more convenient for you than London Road, (...) and you supposed that I would be able to infer your intention by consulting Bradshaw, and noting a 12.5 arrival from Leicester at Marylebone. (shrink)

Thomas S. Kuhn's classic book is now available with a new index.

Das Wissen, welches zuerst oder unmittelbar unser Gegenstand ist, kann kein anderes sein als dasjenige, welches selbst unmittelbares Wissen, Wissen des Unmittelbaren oder Seienden ist. Wir haben uns ebenso unmittelbar oder aufnehmend zu verhalten, also nichts an ihm, wie es sich darbietet, zu verändern, und von dem Auffassen das Begreifen abzuhalten.Der konkrete Inhalt der sinnlichen Gewißheit läßt sie unmittelbar als die reichste Erkenntnis, ja als eine Erkenntnis von unendlichem Reichtum erscheinen, für welchen ebensowohl wenn wir im Raume und in der (...) Zeit, als worin er sich ausbreitet, hinaus-, als wenn wir uns ein Stück aus dieser Fülle nehmen, und durch Teilung in dasselbe hineingehen, keine Grenze zu finden ist. (shrink)

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox (...) fail to account for the predictive utility of universal laws like All ravens are black. (shrink)

A common view among statisticians is that convergence (which statisticians call consistency) is a necessary property of an inference rule or estimator. In this paper, this view is challenged by appeal to an example in which a rule of inference has a likelihood rationale but is not convergent. The example helps clarify the significance of the likelihood concept in statistical inference.

I present a justification for the intution that more-varied data are more valuable than the same number of less-varied data by showing that the more-varied data help to improve the accuracy of our predictions.

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)

Dr Edwards' stimulating and provocative book advances the thesis that the appropriate axiomatic basis for inductive inference is not that of probability, with its addition axiom, but rather likelihood - the concept introduced by Fisher as a measure of relative support amongst different hypotheses. Starting from the simplest considerations and assuming no more than a modest acquaintance with probability theory, the author sets out to reconstruct nothing less than a consistent theory of statistical inference in science.

Slobodkin proposes that the best intellectual work is done as if it were a game on a simplified playing field.