Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making: 310 (Lecture Notes in Economics and Mathematical Systems) - Softcover
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In the literature of decision analysis it is traditional to rely on the tools provided by probability theory to deal with problems in which uncertainty plays a substantive role. In recent years, however, it has become increasingly clear that uncertainty is a mul tifaceted concept in which some of the important facets do not lend themselves to analysis by probability-based methods. One such facet is that of fuzzy imprecision, which is associated with the use of fuzzy predicates exemplified by small, large, fast, near, likely, etc. To be more specific, consider a proposition such as "It is very unlikely that the price of oil will decline sharply in the near future," in which the italicized words play the role of fuzzy predicates. The question is: How can one express the mean ing of this proposition through the use of probability-based methods? If this cannot be done effectively in a probabilistic framework, then how can one employ the information provided by the proposition in question to bear on a decision relating to an investment in a company engaged in exploration and marketing of oil? As another example, consider a collection of rules of the form "If X is Ai then Y is B,," j = 1, . . . , n, in which X and Yare real-valued variables and Ai and Bi are fuzzy numbers exemplified by small, large, not very small, close to 5, etc.
Reseña del editor:
This volume brings together for the first time articles by well-known experts focusing on the topic of the joint occurrence of imprecision, dealt with in terms of fuzzy sets and possibility theory, and randomness, dealt with in terms of probability theory, in a wide spectrum of decision making problems. In the introductory section, some basic issues related to decision making under uncertainty and fuzziness are discussed in some survey-type papers. The second part is devoted to basic theoretical issues, including uncertainty measures, fuzzy measures, fuzzy random variables, fuzzy statistics and evaluation and aggregation of imprecise and uncertain evidence. The third part presents the concepts of a stochastic fuzzy set and a probabilistic set which are basically some fuzzy sets involving random aspects. The fourth part presents a wide spectrum of models in which some aspects of fuzziness and randomness co-occur. It includes papers on fuzzy statistical decision making, fuzzy stochastic and statistical dominance, probabilistic-set-based decision making, optimization under fuzziness and randomness and fuzzy dynamic programming with stochastic systems. The fifth and final part deals with applications in economy and finance, nuclear reactor protection, classification and pattern recognition, reliability and earthquake forecasting. The reader is provided with a set of tools which enable him to deal with diverse decision problems in which imprecision and uncertainty jointly occur.
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- VerlagSpringer Berlin Heidelberg
- Erscheinungsdatum1988
- ISBN 10 3540500057
- ISBN 13 9783540500056
- EinbandTapa blanda
- Anzahl der Seiten412
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Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making
Verlag:
Springer Berlin Heidelberg
(1988)
ISBN 10: 3540500057
ISBN 13: 9783540500056
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Buchbeschreibung Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the literature of decision analysis it is traditional to rely on the tools provided by probability theory to deal with problems in which uncertainty plays a substantive role. In recent years, however, it has become increasingly clear that uncertainty is a mul tifaceted concept in which some of the important facets do not lend themselves to analysis by probability-based methods. One such facet is that of fuzzy imprecision, which is associated with the use of fuzzy predicates exemplified by small, large, fast, near, likely, etc. To be more specific, consider a proposition such as 'It is very unlikely that the price of oil will decline sharply in the near future,' in which the italicized words play the role of fuzzy predicates. The question is: How can one express the mean ing of this proposition through the use of probability-based methods If this cannot be done effectively in a probabilistic framework, then how can one employ the information provided by the proposition in question to bear on a decision relating to an investment in a company engaged in exploration and marketing of oil As another example, consider a collection of rules of the form 'If X is Ai then Y is B,' j = 1, . . . , n, in which X and Yare real-valued variables and Ai and Bi are fuzzy numbers exemplified by small, large, not very small, close to 5, etc. Artikel-Nr. 9783540500056
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