On Measuring Uncertainty and UncertaintyBased Information: Recent Developments
 George J. Klir,
 Richard M. Smith
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It is shown in this paper how the emergence of fuzzy set theory and the theory of monotone measures considerably expanded the framework for formalizing uncertainty and suggested many new types of uncertainty theories. The paper focuses on issues regarding the measurement of the amount of relevant uncertainty (predictive, prescriptive, diagnostic, etc.) in nondeterministic systems formalized in terms of the various uncertainty theories. It is explained how information produced by an action can be measured by the reduction of uncertainty produced by the action. Results regarding measures of uncertainty (and uncertaintybased information) in possibility theory, Dempster–Shafer theory, and the various theories of imprecise probabilities are surveyed. The significance of these results in developing sound methodological principles of uncertainty and uncertaintybased information is discussed.
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 Title
 On Measuring Uncertainty and UncertaintyBased Information: Recent Developments
 Journal

Annals of Mathematics and Artificial Intelligence
Volume 32, Issue 14 , pp 533
 Cover Date
 20010801
 DOI
 10.1023/A:1016784627561
 Print ISSN
 10122443
 Online ISSN
 15737470
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 uncertainty
 uncertaintybased information
 nonspecificity
 conflict
 possibility theory
 Dempster–Shafer theory
 theories of imprecise probabilities
 fuzzy sets
 monotone measures
 rough sets
 Industry Sectors
 Authors

 George J. Klir ^{(1)}
 Richard M. Smith ^{(1)}
 Author Affiliations

 1. Center for Intelligent Systems and Department of Systems Science and Industrial Engineering, Binghamton University – SUNY, Binghamton, NY, 13902, USA