On the uniqueness of possibilistic measure of uncertainty and information
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
Information semantics of possibilistic uncertainty
Fuzzy Sets and Systems - Special issue on fuzzy information processing
Measures of component importance in reliability theory
Computers and Operations Research - Special issue: reliability and quality control
Uncertainty in fault tree analysis: a fuzzy approach
Fuzzy Sets and Systems - Special issue on fuzzy methodology in system failure engineering
Properties of measures of information in evidence and possibility theories
Fuzzy Sets and Systems
Possibilistic information theory: a coding theoretic approach
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Possibilistic entropies and the compression of possibilistic data
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Fuzzy Sets and Systems
A review of possibilistic approaches to reliability analysis and optimization in engineering design
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: applications and services
Uncertainty representation using fuzzy measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Subjective factors and nonlinear characteristics, inherent in the importance identification for a fault tree in the reliability and risk analysis, make it necessary for fuzzy or possibilistic approaches to accommodate the quantificational assessment of epistemic uncertainty in a practical problem when data and information are very limited. After investigating the intuitive interpretations, possibilistic information semantics, measure-theoretic terms and entropy-like models, a new axiomatic index of importance measure for fault trees is proposed based upon possibilistic information entropy, which adopts the possibilistic assumption in place of the probabilistic one. An example of the fault tree is provided along with the concordance analysis and other discussions. The more conservative numerical results of importance rankings that involve more choices could be viewed as “soft” fault identification under a certain expected value. Finally, possible extension to the evidence space and further research directions are discussed.