Decision support system

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

Abstract: Preface 1. Adaptive decision behaviour: an introduction 2. Contingencies in decision making 3. Deciding how to decide: an effort/accuracy framework 4. Studying contingent decisions: an integrated methodology 5. Constructive processes in decision making 6. Why may adaptivity fail? 7. Improving decisions and other practical matters 8. The adaptive decision maker: a look backward and a look forward Appendix Footnotes Bibliography.

Abstract: The past decade has brought advanced information technologies, which include electronic messaging systems, executive information systems, collaborative systems, group decision support systems, and other technologies that use sophisticated information management to enable multiparty participation in organization activities. Developers and users of these systems hold high hopes for their potential to change organizations for the better, but actual changes often do not occur, or occur inconsistently. We propose adaptive structuration theory (AST) as a viable approach for studying the role of advanced information technologies in organization change. AST examines the change process from two vantage points: (1) the types of structures that are provided by advanced technologies, and (2) the structures that actually emerge in human action as people interact with these technologies. To illustrate the principles of AST, we consider the small group meeting and the use of a group decision support system (GDSS). A GDS...