Annals of Mathematics and Artificial Intelligence

, Volume 32, Issue 1, pp 5–33

On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments

Authors

  • George J. Klir
    • Center for Intelligent Systems and Department of Systems Science and Industrial EngineeringBinghamton University – SUNY
  • Richard M. Smith
    • Center for Intelligent Systems and Department of Systems Science and Industrial EngineeringBinghamton University – SUNY
Article

DOI: 10.1023/A:1016784627561

Cite this article as:
Klir, G.J. & Smith, R.M. Annals of Mathematics and Artificial Intelligence (2001) 32: 5. doi:10.1023/A:1016784627561

Abstract

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 uncertainty-based 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 uncertainty-based information is discussed.

uncertaintyuncertainty-based informationnonspecificityconflictpossibility theoryDempster–Shafer theorytheories of imprecise probabilitiesfuzzy setsmonotone measuresrough sets
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Copyright information

© Kluwer Academic Publishers 2001