Models as Mediators discusses the ways in which models function in modern science, particularly in the fields of physics and economics. Models play a variety of roles in the sciences: they are used in the development, exploration and application of theories and in measurement methods. They also provide instruments for using scientific concepts and principles to intervene in the world. The editors provide a framework which covers the construction and function of scientific models, and explore the ways in which they (...) enable us to learn about both theories and the world. The contributors to the volume offer their own individual theoretical perspectives to cover a wide range of examples of modelling, from physics, economics and chemistry. These papers provide ideal case-study material for understanding both the concepts and typical elements of modelling, using analytical approaches from the philosophy and history of science. (shrink)

... the topic of 'meaning' is the one topic discussed in philosophy in which there is literally nothing but 'theory' - literally nothing that can be labelled or even ridiculed as the 'common sense view'. Putnam, 'The Meaning of Meaning' This book explores some truths behind the truism that experimentation is a hallmark of scientific activity. Scientists' descriptions of nature result from two sorts of encounter: they interact with each other and with nature. Philosophy of science has, by and large, (...) failed to give an account of either sort of interaction. Philosophers typically imagine that scientists observe, theorize and experiment in order to produce general knowledge of natural laws, knowledge which can be applied to generate new theories and technologies. This view bifurcates the scientist's world into an empirical world of pre-articulate experience and know how and another world of talk, thought and argument. Most received philosophies of science focus so exclusively on the literary world of representations that they cannot begin to address the philosophical problems arising from the interaction of these worlds: empirical access as a source of knowledge, meaning and reference, and of course, realism. This has placed the epistemological burden entirely on the predictive role of experiment because, it is argued, testing predictions is all that could show that scientists' theorizing is constrained by nature. Here a purely literary approach contributes to its own demise. The epistemological significance of experiment turns out to be a theoretical matter: cruciality depends on argument, not experiment. (shrink)

In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose (...) the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. (shrink)

Biographical studies have shown that visual mental imagery plays a significant role in the conduct of scientific research, particularly in the generation of hypotheses. But the nature of visual mental imagery and its participation in abductive inference is not systematically understood. This paper discusses examples of visual abductive reasoning by archaeologists, analyzing them according to the visual information and the process of inference employed. This work supports the conclusion that visual abduction is useful to scientists under certain conditions and that (...) it is amenable to detailed study. (shrink)

In this path-breaking work, Paul Thagard draws on history and philosophy of science, cognitive psychology, and the field of artificial intelligence to develop a ...

Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, ...

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

Confronted with problems or situations that do not yield toknown theories and world views, scientists and students are alike. Theyare rarely able to directly build a model or a theory thereof. Rather,they must find ways to make sense of the circumstances using theircurrent knowledge and adjusting what is recognized in the process. Thisway of thinking, using past ways of perceiving the physical world tobuild new ones does not follow a logical path and cannot be described astheory revision. Likewise, in many (...) situations it is awkward, indeedoften impossible, to resort to analogical reasoning to account for it.This paper presents a new mechanism, called `tunnel effect', that mayexplain, in part, how scientists and students reason while constructinga new conceptual domain. `Tunnel effect' is also contrasted withanalogical reasoning. (shrink)

The total irrelevance of absolute space to scientific observation and experiment led him early to a most radical conclusion: experience cannot teach us anything about the true structure of space; consequently, the choice of a geometry for the ...

In this book, Newell makes the case for unified theories by setting forth a candidate.

The first edition of The Scientific Background to Modern Philosophy took the dialogue of science and philosophy from Aristotle through to Newton. This second edition adds eight chapters, taking the dialogue through the Enlightenment and up to Darwin. This anthology is an attempt to help bridge the gap between the history of science and the history of philosophy.

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics (...) as the nature of concepts, hypothesis formation, analogy, and theory justification. (shrink)

Reprint of the original, first published in 1869.

There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book’s contributors are researchers active in the area of creative reasoning in science and technology.