Computational Philosophy of Science by Paul Thagard

By Paul Thagard

By way of making use of examine in man made intelligence to difficulties within the philosophy of technological know-how, Paul Thagard develops an exhilarating new method of the research of medical reasoning. He makes use of computational principles to make clear how clinical theories are found, evaluated, and utilized in motives. He describes a close computational version of challenge fixing and discovery that offers a conceptually wealthy but rigorous replacement to bills of clinical wisdom in accordance with formal common sense. The version is used to light up such themes because the nature of options, speculation formation, analogy, and thought justification.Following a critique of the choice account of clinical improvement provided by means of evolutionary epistemology, Thagard discusses philosophical concerns pertaining to reasoning, fact, and the justification of medical tools. He applies his common conclusions approximately technology and pseudoscience to the fields of psychology and synthetic intelligence, and explores the aptitude relevance of computational types to our realizing of the interrelations of conception and test and of the significance of team rationality in science."Computational Philosophy of technology" has been made available to readers from diverse disciplines via an appendix that comes with tutorials on crucial philosophical, computational, and mental topics.Paul Thagard is a study scientist on the Princeton collage Cognitive technological know-how Laboratory. he's coauthor, with John H. Holland, Keith J. Holyoak, and Richard E. Nisbett, of "Induction: tactics of Inference, studying, and Discovery (MIT Press/Bradford Books). A Bradford e-book.

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So anything that can be done computationally on a digital computer can be done on a Turing machine. But this fact is not of much interest for under­ standing intelligence because Turing machines are slow and torturous to program. The design of intelligent systems, whether by natural selection or human engineers, unavoidably takes into account speed of operations. Hence the current wave of research on how parallel architectures for computers can speed up processing is highly relevant for understanding the nature of mind (see chapter 10 and Thagard, 1986).

The question of whether a computational model of thinking must have separate structures corresponding to concepts is con­ troversial, and the important cognitive architectures of Anderson (1983) and Laird, Rosenbloom, and Newell ( 1986) do not have them. I shall argue that they are an important part of a theory of cognition. 2. Problem Solving PI's central activity is problem solving. Given a set of starting conditions and goals, it fires rules that will lead from the starting conditions to the goals.

Axiom systems could be said to be psychologically real if we viewed scien­ tists as solving problems by straightforwardly making deductions from sets of propositions, but we saw in the last chapter the need for a much richer approach. In sum, the positivist account is not very practically adequate. The most influential criticisms of the positivist account have come from historians and historically oriented philosophers (Kuhn, 1970b; Toulmin, 1953; Hanson, 1958). They charge that logical positivists neglected the dramatic extent of conceptual change in the history of science, a neglect stemming in part from the supposition that the meaning of theoretical tenns derives from partial interpretation through observational conse­ quences.

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