F.A. Hayek studied at the University of Vienna, where he became both a Doctor of Law and a Doctor of Political Science. After several years in the Austrian civil service, he was made the first diector of the Austrian Institute for Business Cycle Research. In 1931 he was appointed Tooke Professor of Economics and Statistics at the London School of Economics, and in 1950 he went to the University of Chicago as Professor of Social and Moral Sciences. He returned to (...) Europe in 1962, to the chair of Economics at the University of Freiburg, where he became Professor Emeritus in 1967. The holder of numerous honorary doctorates, and a member of the British Academy, Hayek was awarded the Nobel Memorial Prize in Economics in 1974. He was created Companion of Honour in 1984. He is the author of some fifteen books, including _Prices and Production, The Pure Theory of Capital, The Road to Serfdom, The Counter-Revolution of Science, The Sensory Order, The Constitution of Liberty, _ _Law, Legislation and Liberty_, and _The Fatal Conceit_. (shrink)

Sir John Hicks is one of the highest-regarded contemporary economists, and it is fitting that the new series of _Critical Assessments of Contemporary Economists_ should commence with his work. Awarded the Nobel Prize for Economics in 1972, Sir John Hicks’ work is extremely wide-ranging, with the list of topics reading almost like an agenda for the whole of modern economics: general equilibrium theory, welfare economics, problems of index numbers, trade cycles, wages and many others. He may, however, be best known (...) to present day economists for having introduced IS-LM curves, now a standard means of Keynesian analysis. A comprehensive, scholarly work, this four-volume set gives students of economics and economic thought immediate access to Sir John Hicks' contributions and shows how his work has been received and modified by others. (shrink)

Debate over the nature of science has recently moved from the halls of academia into the public sphere, where it has taken shape as the "science wars." At issue is the question of whether scientific knowledge is objective and universal or socially mediated, whether scientific truths are independent of human values and beliefs. Ronald Giere is a philosopher of science who has been at the forefront of this debate from its inception, and Science without Laws offers a much-needed mediating (...) perspective on an increasingly volatile line of inquiry. Giere does not question the major findings of modern science: for example, that the universe is expanding or that inheritance is carried by DNA molecules with a double helical structure. But like many critics of modern science, he rejects the widespread notion of science--deriving ultimately from the Enlightenment--as a uniquely rational activity leading to the discovery of universal truths underlying all natural phenomena. In these highly readable essays, Giere argues that it is better to understand scientists as merely constructing more or less abstract models of limited aspects of the world. Such an understanding makes possible a resolution of the issues at stake in the science wars. The critics of science are seen to be correct in rejecting the Enlightenment idea of science, and its defenders are seen to be correct in insisting that science does produce genuine knowledge of the natural world. Giere is utterly persuasive in arguing that to criticize the Enlightenment ideal is not to criticize science itself, and that to defend science one need not defend the Enlightenment ideal. Science without Laws thus stakes out a middle ground in these debates by showing us how science can be better conceived in other ways. (shrink)

This latest volume in the eminent Minnesota Studies in the Philosophy of Science series examines the main features of the intellectual milieu from which logical empiricism sprang, providing the first critical exploration of this context by ...

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)

I begin by arguing that a consistent general naturalism must be understood in terms of methodological maxims rather than metaphysical doctrines. Some specific maxims are proposed. I then defend a generalized naturalism from the common objection that it is incapable of accounting for the normative aspects of human life, including those of scientific practice itself. Evolutionary naturalism, however, is criticized as being incapable of providing a sufficient explanation of categorical moral norms. Turning to the epistemological norms of science itself, particularly (...) those governing the empirical testing of specific models, I argue that these should be regarded as conditional rather than categorical and that, as such, can be given a naturalistic justification. The justification, however, is more cognitive than evolutionary. The historical development of science is found to be a better place for applying evolutionary ideas. After briefly considering the possibility of a naturalistic understanding of mathematics and logic, I turn to the problem of reconciling scientific realism with an evolutionary picture of scientific development. The solution, I suggest, is to understand scientific knowledge as being “perspectival” rather than absolutely objective. I first argue that scientific observation, whether by humans or instruments, is perspectival. This argument is extended to scientific theorizing which is regarded not as the formulation of universal laws of nature but as the construction of principles to be used in the construction of models to be applied to specific natural systems. The application of models, however, is argued to be not merely opportunistic but constrained by the methodological presumption that we live in a world with a definite causal structure even though we can understand it only from various perspectives. (shrink)

This paper is a contribution to that part of science studies known as 'the cognitive study of science'. The general goal of such studies is to understand cogni-.

In earlier works, I have argued that it is useful to think of much scientific activity, particularly in experimental sciences, as involving the operation of distributed cognitive systems, as these are understood in the contemporary cognitive sciences. Introducing a notion of distributed cognition, however, invites consideration of whether, or in what way, related cognitive activities, such as knowing, might also be distributed. In this paper I will argue that one can usefully introduce a notion of distributed cognition without attributing other (...) cognitive attributes, such as knowing, let alone having a mind or being conscious, to distributed cognitive systems. I will first briefly introduce the cognitive science understanding of distributed cognition, partly so as to distinguish full-blown distributed cognition from mere collective cognition.1. (shrink)

Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a (...) target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system. (shrink)

There is no best scientific model of anything; there are only models more or less good for different purposes. Thus, there is no general answer to the question of whether one should model biological behavior using computer simulations or robots. It all depends on what one wants to learn. This is not a question about models, but about scientific goals.

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)

Understanding Scientific Reasoning, Fifth Edition, develops critical reasoning skills and guides students in the improvement of their scientific and technological literacy. The authors teach students how to understand and critically evaluate the scientific information they encounter in both textbooks and the popular media. With its focus on scientific pedagogy, Understanding Scientific Reasoning helps students learn how to examine scientific reports with a reasonable degree of sophistication. The book also explains how to reason through case studies using the same informal logic (...) skills employed by scientists and to analyse a complex series of propositions and hypotheses using sound scientific reasoning--Publisher's blurb. (shrink)

I begin with a representative example of a contemporary scientific activity, observations using the Hubble Space Telescope, and ask what approaches within the cognitive sciences seem most fruitful as aids in developing an overall account of this sort of scientific activity. After presenting the Hubble Space Telescope System and a recent result, I consider applying a standard computational paradigm to this system. I find difficulties in identifying an appropriate cognitive agent and in making a suitable place for the instrumentation that (...) constitutes such a large part of the whole system. I next consider applying the notion of distributed cognition as developed by Hutchins (1995), and then return to the question whether The Hubble System, understood as a distributed cognitive system, should be regarded as a computational system. I find a large computational component, but also an important part, the Hubble Telescope itself, that seems better characterized as a dynamic system than as a computational system. Moreover, the group of scientists interpreting the images produced by the system seem best thought of as a human/cultural system along the lines advocated by those developing a cognitive (Lakoff, 1987) or usage-based (Tomasello, 2003) approach to language acquisition and language use. I argue next that, while cognition may be theorized as distributed among both humans and instruments, there is no need to introduce into cognitive science a notion of distributed knowledge beyond simple collective knowledge. Even less is there any need to introduce notions of distributed mind or distributed consciousness. The result is that the agency involved in distributed cognitive systems remains simply human agency as ordinarily conceived. I conclude that distributed cognitive systems like The Hubble System are hybrid systems composed partly of dynamic physical systems, partly of computational systems, and partly of human cultural systems. (shrink)

I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. (...) This whole approach is further supported by a brief exposition of some recent work in cognitive, or usage-based, linguistics. Finally, with all the above as background, I criticize the recently much discussed idea that claims involving scientific models are really fictions. (shrink)

I appreciate Norton Wise’s comparison of my project in Explaining Science (1988) with that of Enlightenment scientists and philosophers. When rejecting one’s immediate philosophical predecessors, it is comforting to be able to portray oneself not as a heretic who has abandoned philosophy, but as a reformer who would return philosophy to the correct path from which his predecessors had strayed. -/- But we cannot simply return to the ideals of the Enlightenment. Some doctrines that were fundamental to the Enlightenment picture (...) of science must be rejected. In particular, I think we must reject the idea that the content of science is encapsulated in universal laws. And we must reject the notion that there are universal principles of rationality that justify our belief in the truth of universal laws. As Wise notes, these latter are typically “postmodern” themes, and, as such, are usually posed in explicit opposition to the modernism of the Enlightenment. It is my view that this opposition must be overcome. The overall project for the philosophy of science now is to develop an image of science that is appropriately postmodern while retaining something of the Enlightenment respect for the genuine accomplishments of modern science. To many the idea of an “enlightened postmodernism” may seem contradictory. I see it merely as an example of postmodern irony. (shrink)

The argument that recent attempts to model technology studies on science studies have consequences for approaches to science studies as well is presented. In particular, the move to technology studies through science studies counts against the existing extreme pictures of science, "enlightenment rationalism," and "constructivisim," which are identified with modernism and postmodernism, respectively. Some components for a moderate "enlightened post-modern synthesis" in naturalism, interest theory, and systems theory are found.

Among philosophers, Don Campbell is best known for his naturalistic, evolutionary approach to epistemology. There can be no doubt, however, that he was a thoroughgoing naturalist in all matters, even though he seems to have had little interest in exploring naturalism as a general philosophical position. He was professionally more interested in the origins and workings of knowledge producing social systems. I am interested in these things too, but also naturalism in general. So my tribute to Don will be to (...) present some aspects of a generalized naturalism that I hope he would have liked. Campbell's naturalism, like his epistemology, would be evolutionary, but, like his realism, would be more. It would be hypothetical, a posit, and not a first principle. And it would be critical, subject to ongoing refinement in light of new experience. Thus my title. It is a title I fancy Don would have liked, perhaps one he himself would have used had he ever taken up naturalism as a general theme. It is much easier to give a negative characterization of naturalism than to provide a positive account of what it.. (shrink)

The principles and practices of research peer review are described. While the principles are fundamentally generic and apply to peer review across the full spectrum of performing institutions as well as to manuscript/proposal/program peer review, the focus of this paper is peer review of proposed and ongoing programs in federal agencies. The paper describes desireable characteristics and important intangible factors in successful peer review. Also presented is a heuristic protocol for the conduct of successful peer review research evaluations and impact (...) assessments. Problems with peer review are then outlined, followed by examples of peer review of proposed and existing programs in selected federal agencies. Some peer review variants, such as the Science Court, are described, and then research requirements to improve peer review are discussed. (shrink)

In arguing a "role for history," Kuhn was proposing a naturalized philosophy of science. That, I argue, is the only viable approach to the philosophy of science. I begin by exhibiting the main general objections to a naturalistic approach. These objections, I suggest, are equally powerful against nonnaturalistic accounts. I review the failure of two nonnaturalistic approaches, methodological foundationism (Carnap, Reichenbach, and Popper) and metamethodology (Lakatos and Laudan). The correct response, I suggest, is to adopt an "evolutionary perspective." This perspective (...) is defended against one recent critic (Putnam). To argue the plausibility of a naturalistic approach, I next sketch a naturalistic account of theories and of theory choice. This account is then illustrated by the recent revolution in geology. In conclusion I return to Kuhn's question about the role of history in developing a naturalistic theory of science. (shrink)

This paper provides a general defense of the idea that the cognitive sciences provide models that are useful for exploring issues that have traditionally occupied philosophers of science. Questions about the nature of theories, for example, are assimilated into studies of the nature of cognitive representations, while questions concerning the choice of theories fall under studies of human judgment and decision making. The implications of adopting "a cognitive approach" are explored, particularly the rejection of foundationist epistemologies which might provide a (...) philosophical justification of science. Instead I suggest a scientific foundation provided by evolutionary biology and the scientific goal of explaining science as a human phenomenon. (shrink)

Among the many contested boundaries in science studies is that between the cognitive and the social. Here, we are concerned to question this boundary from a perspective within the cognitive sciences based on the notion of distributed cognition. We first present two of many contemporary sources of the notion of distributed cognition, one from the study of artificial neural networks and one from cognitive anthropology. We then proceed to reinterpret two well-known essays by Bruno Latour, ‘Visualization and Cognition: Thinking with (...) Eyes and Hands’ and ‘Circulating Reference: Sampling the Soil in the Amazon Forest’. In both cases we find the cognitive and the social merged in a system of distributed cognition without any appeal to agonistic encounters. For us, results do not come to be regarded as veridical because they are widely accepted; they come to be widely accepted because, in the context of an appropriate distributed cognitive system, their apparent veracity can be made evident to anyone with the capacity to understand the workings of the system. (shrink)

This paper explores a new reason for preferring a model-theoretic approach to understanding the nature of scientific theories. Identifying the models in philosophers' model-theoretic accounts of theories with the concepts in cognitive scientists' accounts of categorization suggests a structure to families of models far richer than has commonly been assumed. Using classical mechanics as an example, it is argued that families of models may be "mapped" as an array with "horizontal" graded structures, multiply hierarchical "vertical" structures, and local "radial" structures. (...) These structures promise important implications for how scientific theories are learned and used in actual scientific practice. (shrink)

Naturalism in the philosophy of science, and philosophy generally, is more an overall approach to the subject than a set of specific doctrines. In philosophy it may be characterized only by the most general ontological and epistemological principles, and then more by what it opposes than by what it proposes.

The similarity of documents in a large database of published Fractals articles was examined for redundancy. Three different text matching techniques were used on published Abstracts to identify redundancy candidates, and predictions were verified by reading full text versions of the redundancy candidate articles. A small fraction of the total articles in the database was judged to be redundant. This was viewed as a lower limit, because it excluded cases where the concepts remained the same, but the text was altered (...) substantially. Far more pervasive than redundant publications were publications that did not violate the letter of redundancy but rather violated the spirit of redundancy. There appeared to be widespread publication maximization strategies. Studies that resulted in one comprehensive paper decades ago now result in multiple papers that focus on one major problem, but are differentiated by parameter ranges, or other stratifying variables. This ‘paper inflation’ is due in large part to the increasing use of metrics (publications, patents, citations, etc) to evaluate research performance, and the researchers’ motivation to maximize the metrics. (shrink)

In Epistemic Cultures (1999), Karin Knorr Cetina argues that different scientific fields exhibit different epistemic cultures. She claims that in high energy physics (HEP) individual persons are displaced as epistemic subjects in favor of experiments themselves. In molecular biology (MB), by contrast, individual persons remain the primary epistemic subjects. Using Ed Hutchins' (1995) account of navigation aboard a traditional US Navy ship as a prototype, I argue that both HEP and MB exhibit forms of distributed cognition. That is, in both (...) fields cognition is distributed across individual persons and complex artifacts. The cognitive system producing the knowledge is heterogeneous. Nevertheless, in both fields we can reserve epistemic agency for the human components of these systems. We do not need to postulate new distributed cognitive agents, let alone ones exhibiting new forms of consciousness. (shrink)

The usual question, “Are models fictions?” is replaced by the question, “Should scientific models be regarded as works of fiction?” This makes it clear that the issue is not one of definition but of interpretation. First one must distinguish between the ontology of scientific models and their function in the practice of science. Theoretical models and works of fiction are ontologically on a par, their both being creations of human imagination. It is their differing functions in practice that makes it (...) inappropriate to regard scientific models as works of fiction. Three reasons for thinking scientific models should be regarded as works of fiction are rejected. First, scientists themselves sometimes invoke the idea of fictions in their discussions of specific models. Second, many scientific models are physically impossible to realize in the real world. Third, regarding scientific models as works of fiction supports a general fictionalist understanding of scientific theories. It is concluded that promoting the general idea that scientific models are works of fiction unnecessarily supports attacks on the legitimacy of science itself. (shrink)

Until very recently it could have been said that most approaches to the philosophy of science were “cognitive.” This includes logical positivism (see logical positivism), as well as later, historically based philosophies of science, such as that of Imre Lakatos (see lakatos). Here the contrast is between the cognitive and the psychological or social dimensions of science. Central to all such “cognitive” approaches is a robust notion of rationality, or rational progress, in the evaluation of scientific theories or research programmes (...) (see theories). Carnap sought an inductive logic that would make the evaluation of hypotheses rational in the way that deductive inference is rational. Lakatos defined rational progress in terms of increasing empirical content. For both it was essential that the philosophy of science exhibit science as a rational, rather than a merely psychological or social, enterprise. (shrink)

Does recent work in the cognitive sciences have any implications for theories or methods employed within the philosophy of science itself? It does if one takes a naturalistic approach in which understanding the nature of representations or judgments of representational success in science requires reference to the cognitive capacities or activities of individual scientists. Here I comment on recent contributions from three areas of the cognitive sciences represented respectively by Paul Churchland's neurocomputational perspective, Nancy Nersessian's cognitive-historical approach, and Paul Thagard's (...) computational philosophy of science. The main general conclusion is that we need to replace traditional linguistic notions of representation in science. (shrink)