This edition includes new essays by philosopher Michael Williams and literary scholar David Bromwich, as well as Rorty's previously unpublished essay "The ...

Reexamines the Western philosophical tradition, looking at the basic concepts of the mind, time, causation, morality, and the self.

This classic remains one of Karl Popper's most wide-ranging and popular works, notable not only for its acute insight into the way scientific knowledge grows, but also for applying those insights to politics and to history.

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.

Required reading at more than 100 colleges and universities throughout North America.

The way in which knowledge progresses, and especially our scientific knowledge, is by unjustified anticipations, by guesses, by tentative solutions to our problems, by conjectures. These conjectures are controlled by criticism: that is, by attempted refutations, which include severely critical tests. They may survive these tests; but they can never be positively justified: they can neither be established as certainly true nor even as 'probable'. Criticism of our conjectures is of decisive importance: by bringing out our mistakes it makes us (...) understand the difficulties of the problems which we try to solve. This is how we become better acquainted with our problem, and able to propose more mature solutions: the very refutation of a theory - that is, of a tentative solution to our problem - is always a step forward that takes us nearer the truth. And this is how we can learn from our mistakes. As we learn from our mistakes our knowledge grows, even though we may never know - that is, know for certain. Since our knowledge can grow, there can be no reason here for despair of reason. And since we can never know for certain, the can be no authority here for any claim to authority, for conceit over our knowledge, or for smugness. The essays and lectures of which this book is composed apply this thesis to many topics, ranging from problems of the philosophy and history of the physical and social sciences to historical and political problems. (shrink)

Many people assume that the claims of scientists are objective truths. But historians, sociologists, and philosophers of science have long argued that scientific claims reflect the particular historical, cultural, and social context in which those claims were made. The nature of scientific knowledge is not absolute because it is influenced by the practice and perspective of human agents. Scientific Perspectivism argues that the acts of observing and theorizing are both perspectival, and this nature makes scientific knowledge contingent, as Thomas Kuhn (...) theorized forty years ago. Using the example of color vision in humans to illustrate how his theory of “perspectivism” works, Ronald N. Giere argues that colors do not actually exist in objects; rather, color is the result of an interaction between aspects of the world and the human visual system. Giere extends this argument into a general interpretation of human perception and, more controversially, to scientific observation, conjecturing that the output of scientific instruments is perspectival. Furthermore, complex scientific principles—such as Maxwell’s equations describing the behavior of both the electric and magnetic fields—make no claims about the world, but models based on those principles can be used to make claims about specific aspects of the world. Offering a solution to the most contentious debate in the philosophy of science over the past thirty years, Scientific Perspectivism will be of interest to anyone involved in the study of science. (shrink)

Beginning with a historical overview of relativism, from Pythagoras in ancient Greece to Derrida and postmodernism, Maria Baghramian explores the resurgence of relativism throughout the history of philosophy. She then turns to the arguments for and against the many subdivisions of relativism, including Kuhn and Feyerabend's ideas of relativism in science, Rorty's relativism about truth, and the conceptual relativism of Quine and Putnam. Baghramian questions whether moral relativism leads to moral indifference or even nihilism, and whether feminist epistemology's concerns about (...) the very notion of objectivity can be considered a form of relativism. She concludes the relativism debate by assessing the recent criticisms such as Quine's argument from translation and Davidson's claim that even the motivations behind relativism are unintelligible. Finding these criticisms lacking, Baghramian proposes a moderate form of pluralism which addresses the legitimate worries that give rise to relativism without incurring charges of nihilism or anarchy. (shrink)

Part I: Inductivism and its Critics:. 1. Some Historical Background: Inductivism, Russell and the Cambridge School, the Vienna Circle and Popper. 2. Popper’s Critique of Inductivism. 3. Duhem’s Critique of Inductivism. Part II: Conventionalism and the Duhem-Quine Thesis:. 4. Poincare’s Conventionalism of 1902. 5. The Duhem Thesis and the Quine Thesis. Part III: The Nature of Observation:. 6. Observation Statements: the Views of Carnap, Neurath, Popper and Duhem. 7. Observation Statements: Some Psychological Findings. Part IV: The Demarcation between Science and (...) Metaphysics:. 8. Is Metaphysics Meaningless? Wittgenstein, the Vienna Circle and Popper’s Critique. 9. Metaphysics in relation to Science: the Views of Popper, Duhem and Quine. 10. Falsification in the light of the Duhem-Quine Thesis. (shrink)

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules are (...) formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems. (shrink)

The hypothesis of the study was that the domains of color and form are structured into nonarbitrary, semantic categories which develop around perceptually salient “natural prototypes.” Categories which reflected such an organization (where the presumed natural prototypes were central tendencies of the categories) and categories which violated the organization (natural prototypes peripheral) were taught to a total of 162 members of a Stone Age culture which did not initially have hue or geometric-form concepts. In both domains, the presumed “natural” categories (...) were consistently easier to learn than the “distorted” categories. Even when not central, natural prototype stimuli tended to be more rapidly learned and more often chosen as the most typical example of the category than were other stimuli. Implications for general differences between natural categories and the artificial categories of concept formation research were discussed. (shrink)

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are (...) convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions. (shrink)

The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non- symbolic models. Conceptual spaces consist of a number of 'quality dimensions' that often are (...) derived from perceptual mechanisms. It will be outlined how conceptual spaces can represent various kind of information and how they can be used to describe concept learning. The connections to prototype theory will also be presented. (shrink)

William R. Caspary makes the case for Dewey as a more discerning and challenging political theorist than this.

In this dissertation, a logical analysis of points of view is presented. It is based on the concept of determinable presented by Johnson in his book Logic. A point of view is a set of Determinables. Determinables generate a many-dimensional conceptual space. Concepts are subsets of this space, and their relations form a lattice. A logical system to present points of view is introduced and proved to be complete. Some applications of this logic are demonstrated (relative identity, scientific change, inductive (...) logic etc.). (shrink)

This article outlines how conceptual spaces theory applies to modeling changes of scientific frameworks when these are treated as spatial structures rather than as linguistic entities. The theory is briefly introduced and five types of changes are presented. It is then contrasted with Michael Friedman’s neo-Kantian account that seeks to render Kuhn’s “paradigm shift” as a communicatively rational historical event of conceptual development in the sciences. Like Friedman, we refer to the transition from Newtonian to relativistic mechanics as an example (...) of “deep conceptual change.” But we take the communicative rationality of radical conceptual change to be available prior to the philosophical meta-paradigms that Friedman deems indispensable for this purpose. (shrink)

A similarity space is a hyperspace in which the dimensions represent various dimensions on which objects may differ. The similarity space theory of concepts is the thesis that concepts are regions of similarity spaces that are somehow realized in the brain. Proponents of such a theory of concepts include Paul Churchland and Peter Gärdenfors. This paper argues that the similarity space theory of concepts is mistaken because regions of similarity spaces cannot serve as the components of judgments. It emerges that (...) although similarity spaces cannot model concepts, they may model a kind of nonconceptual representation. (shrink)

This book explores the 'self-concept', its cultural, psychopathological and philosophical implications.

The significance of Friedrich Nietzsche for twentieth century culture is now no longer a matter of dispute. He was quite simply one of the most influential of modern thinkers. The opening essay of this 1996 Companion provides a chronologically organised introduction to and summary of Nietzsche's published works, while also providing an overview of their basic themes and concerns. It is followed by three essays on the appropriation and misappropriation of his writings, and a group of essays exploring the nature (...) of Nietzsche's philosophy and its relation to the modern and post-modern world. The final contributions consider Nietzsche's influence on the twentieth century in Europe, the USA, and Asia. New readers and non-specialists will find this the most convenient, accessible guide to Nietzsche currently available. Advanced students and specialists will find a conspectus of recent developments in the interpretation of Nietzsche. (shrink)

This dissertation examines and discusses some phenomena related to the geometric representation of similarity. It takes its inspiration from the existing body of empirical research within the fields of perceptual and cognitive psychology, but also connects to certain areas of machine learning. The problems discussed concern the modelling of information integration behavior when concepts like asymmetry, integrality, separability and familiarity are taken into account. The dissertation investigates how these phenomena can be modelled using geometric representations in order to increase the (...) descriptive power of the models. The main conclusions that can be drawn from this dissertation are that the descriptive powers of geometric models can be increased in a number of ways: a) By augmenting traditional geometric models with parameters of prominence they can reflect asymmetric similarity at least as well as previously known asymmetric models which include more parameters. b) Some specific metric, for instance, the Euclidean metric, should not be used merely by tradition. If there is reason to believe that there are groupings of dimensions such that the most descriptive metric differs between groups, the distance may be better described with a combination rule adding the contribution of each group/subspace together. c) Aspects of familiarity with stimuli should be taken into consideration, even for familiarity built up during a short period of time. When taking familiarity into consideration it may be possible to describe information integration over time with a finer granularity. Furthermore, by focusing on the part of the phenomenological data that reflect a more stable behavior, which seems to occur first when subjects are sufficiently familiar with the stimuli, the more stable behavior can be more accurately described. (shrink)