Idealism is an ontological view, a view about what sorts of things there are in the universe. Idealism holds that what there is depends on our own mental structure and activity. Berkeley of course held that everything was mental; Kant held the more complex view that there was an important distinction between the mental and the physical, but that the structure of the empirical world depended on the activities of minds. Despite radical differences, idealists like Berkeley and Kant share what (...) Ralph Barton Perry called "the cardinal principle of idealism," namely, the principle that "being is dependent on the knowing of it."1 I believe that Hilary Putnam intends his "internal realism" to be a version of idealism in this broad sense; although many of his arguments concern semantic notions like truth and reference, he takes these semantic arguments to have ontological consequences. This is strongly suggested, for instance, by his claim that "'objects' themselves are as much made as discovered, as much products of our conceptual invention as of the 'objective' factor in experience."2 Or again there is this rather Kantian metaphor: "the mind and the world jointly make up the mind and the world."3 But just what is Putnam's ontology? (shrink)

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

Hilary Putnam deals in this book with some of the most fundamental persistent problems in philosophy: the nature of truth, knowledge and rationality. His aim is to break down the fixed categories of thought which have always appeared to define and constrain the permissible solutions to these problems.

What is the nature of the social sciences? What kinds of knowledge can they—and should they—hope to create? Are objective viewpoints possible and can universal laws be discovered? Questions like these have been asked with increasing urgency in recent years, as some philosophers and researchers have perceived a "crisis" in the social sciences. Metatheory in Social Science offers many provocative arguments and analyses of basic conceptual frameworks for the study of human behavior. These are offered primarily by practicing researchers and (...) are related to problems in disciplines as diverse as sociology, psychology, psychiatry, anthropology, and philosophy of science. While various points of view are expressed in these nineteen essays, they have in common several themes, including the comparison of social and natural science, the role of knowledge in meeting the demands of society and its pressing problems, and the nature and role of subjectivity in science. Some authors hold that subjectivity cannot be studied scientifically; others argue that it can and must be if progress in knowledge is to be made. The essays demonstrate the philosophical pluralism they discuss and give a wide range of alternative positions on the future of the social and behavioral sciences in a postpositivist intellectual world. (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.

Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. It is often criticized for a lack of objectivity: it opens the door to the influence of values and biases, evidence judgments can vary substantially between scientists, it is not suited for informing policy decisions. My paper rebuts these concerns by connecting the debates on scientific objectivity and statistical method. First, I show that the above concerns arise equally for standard frequentist inference with null hypothesis significance tests. Second, (...) the criticisms are based on specific senses of objectivity with unclear epistemic value. Third, I show that Subjective Bayesianism promotes other, epistemically relevant senses of scientific objectivity—most notably by increasing the transparency of scientific reasoning. (shrink)