Knowledge Science

Knowledge science is an emerging discipline resulting from the demands of a knowledge-based economy and information revolution. Explaining how to improve our knowledge-based society, Knowledge Science: Modeling the Knowledge Creation Process addresses problems in collecting, synthesizing, coordinating, and creating knowledge. The book introduces several key concepts in knowledge science: Knowledge technology, which encompasses classification, representation, modeling, identification, acquisition, searching, organization, storage, conversion, and dissemination Knowledge management, which covers three different yet related areas (knowledge assets, knowing processes, knower relations) Knowledge discovery and data mining, which combine databases, statistics, machine learning, and related areas to discover and extract valuable knowledge from large volumes of data Knowledge synthesis, knowledge justification, and knowledge construction, which are important in solving real-life problems Specialists in decision science, artificial intelligence, systems engineering, behavioral science, and management science, the book’s contributors present their own original ideas, including an Oriental systems philosophy, a new episteme in the knowledge-based society, and a theory of knowledge construction. They emphasize the importance of systemic thinking for developing a better society in the current knowledge-based era.

“The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.

Conventional wisdom has it that the sciences, properly pursued, constitute a pure, value-free method of obtaining knowledge about the natural world. In light of the social and normative dimensions of many scientific debates, Helen Longino finds that general accounts of scientific methodology cannot support this common belief. Focusing on the notion of evidence, the author argues that a methodology powerful enough to account for theories of any scope and depth is incapable of ruling out the influence of social and cultural values in the very structuring of knowledge. The objectivity of scientific inquiry can nevertheless be maintained, she proposes, by understanding scientific inquiry as a social rather than an individual process. Seeking to open a dialogue between methodologists and social critics of the sciences, Longino develops this concept of "contextual empiricism" in an analysis of research programs that have drawn criticism from feminists. Examining theories of human evolution and of prenatal hormonal determination of "gender-role" behavior, of sex differences in cognition, and of sexual orientation, the author shows how assumptions laden with social values affect the description, presentation, and interpretation of data. In particular, Longino argues that research on the hormonal basis of "sex-differentiated behavior" involves assumptions not only about gender relations but also about human action and agency. She concludes with a discussion of the relation between science, values, and ideology, based on the work of Habermas, Foucault, Keller, and Haraway.

Language is a human universal reflecting our deeply social nature. Among its essential functions, language enables us to quickly and efficiently share information. We tell each other that many things are true—that is, we routinely make assertions. Information shared this way plays a critical role in the decisions and plans we make. In Knowledge and the Norm of Assertion, a distinguished philosopher and cognitive scientist investigates the rules or norms that structure our social practice of assertion. Combining evidence from philosophy, psychology, and biology, John Turri shows that knowledge is the central norm of assertion and explains why knowledge plays this role. Concise, comprehensive, non-technical, and thoroughly accessible, this volume quickly brings readers to the cutting edge of a major research program at the intersection of philosophy and science. It presupposes no philosophical or scientific training. It will be of interest to philosophers and scientists, is suitable for use in graduate and undergraduate courses, and will appeal to general readers interested in human nature, social cognition, and communication.

Evolution of Knowledge Science: Myth to Medicine: Intelligent Internet-Based Humanist Machines explains how to design and build the next generation of intelligent machines that solve social and environmental problems in a systematic, coherent, and optimal fashion. The book brings together principles from computer and communication sciences, electrical engineering, mathematics, physics, social sciences, and more to describe computer systems that deal with knowledge, its representation, and how to deal with knowledge centric objects. Readers will learn new tools and techniques to measure, enhance, and optimize artificial intelligence strategies for efficiently searching through vast knowledge bases, as well as how to ensure the security of information in open, easily accessible, and fast digital networks. Author Syed Ahamed joins the basic concepts from various disciplines to describe a robust and coherent knowledge sciences discipline that provides readers with tools, units, and measures to evaluate the flow of knowledge during course work or their research. He offers a unique academic and industrial perspective of the concurrent dynamic changes in computer and communication industries based upon his research. The author has experience both in industry and in teaching graduate level telecommunications and network architecture courses, particularly those dealing with applications of networks in education. Presents a current perspective of developments in central, display, signal, and graphics processor-units as they apply to designing knowledge systems Offers ideas and methodologies for systematically extending data and object processing in computing into other disciplines such as economics, mathematics, and management Provides best practices and designs for engineers alongside case studies that illustrate practical implementation ideas across multiple domains

What Is Scientific Knowledge? is a much-needed collection of introductory-level chapters on the epistemology of science. Renowned historians, philosophers, science educators, and cognitive scientists have authored 19 original contributions specifically for this volume. The chapters, accessible for students in both philosophy and the sciences, serve as helpful introductions to the primary debates surrounding scientific knowledge. First-year undergraduates can readily understand the variety of discussions in the volume, and yet advanced students and scholars will encounter chapters rich enough to engage their many interests. The variety and coverage in this volume make it the perfect choice for the primary text in courses on scientific knowledge. It can also be used as a supplemental book in classes in epistemology, philosophy of science, and other related areas. Key features: * an accessible and comprehensive introduction to the epistemology of science for a wide variety of students (both undergraduate- and graduate-level) and researchers * written by an international team of senior researchers and the most promising junior scholars * addresses several questions that students and lay people interested in science may already have, including questions about how scientific knowledge is gained, its nature, and the challenges it faces.

How the tools of STS can be used to understand art and science and the practices of these knowledge-making communities. In Art, Science, and the Politics of Knowledge, Hannah Star Rogers suggests that art and science are not as different from each other as we might assume. She shows how the tools of science and technology studies (STS) can be applied to artistic practice, offering new ways of thinking about people and objects that have largely fallen outside the scope of STS research. Arguing that the categories of art and science are labels with specific powers to order social worlds—and that art and science are best understood as networks that produce knowledge—Rogers shows, through a series of cases, the similarities and overlapping practices of these knowledge communities. The cases, which range from nineteenth-century artisans to contemporary bioartists, illustrate how art can provide the basis for a new subdiscipline called art, science, and technology studies (ASTS), offering hybrid tools for investigating art–science collaborations. Rogers’s subjects include the work of father and son glassblowers, the Blaschkas, whose glass models, produced in the nineteenth century for use in biological classification, are now displayed as works of art; the physics photographs of documentary photographer Berenice Abbott; and a bioart lab that produces work functioning as both artwork and scientific output. Finally, Rogers, an STS scholar and contemporary art–science curator, draws on her own work to consider the concept of curation as a form of critical analysis.

This book constitutes the proceedings of the 5th International Conference on Knowledge Science, Engineering and Management, KSEM 2011, held in Irvine, CA, USA, in December 2011. The 34 revised full papers presented together with 7 short papers were carefully reviewed and selected from numerous submissions.