Uncertainty-Based Information

Information is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.

Key developments and results in the area of generalized information theory are introduced This book contains information, which had until now only been available scattered in journals and conference proceedings

Auteur
George J. Klir is currently Distinguished Professor of Systems Science at Binghamton University, SUNY. Since he immigrating to the U.S. in 1966, he has held positions at UCLA, Fairleigh Dickinson University, and Binghamton University. He is a Life Fellow of IEEE, IFSA, and the Netherlands Institute for Advanced Studies. He has served as president of SGSR, IFSR, NAFIPS, and IFSA.

Contenu
Introduction : Significance of Uncertainty; Uncertainty and Information.- Uncertainty Formalizations : Classical Sets: Terminology and Notation; Fuzzy Set Theory; Fuzzy Measure Theory; Evidence Theory; Probability Theory; Possibility Theory; Overview of Uncertainty Theories.- Uncertainty Measures : Nonspecifity; Conflict; Aggregate Uncertainty in Evidence Theory; Fuzziness; Summary of Uncertainty Measures.- Principles of Uncertainty : Principle of Minimum Uncertainty; Principle of Maximum Uncertainty; Principle of Uncertainty Invariance; Summary of Uncertainty Principles.- Conclusions .