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Titlebook: Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making; Janusz Kacprzyk,Mario Fedrizzi Book 1988 Springer-Verlag Be

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Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making978-3-642-46644-1Series ISSN 0075-8442 Series E-ISSN 2196-9957
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Klaus Dürrschnabel,Rolf Dürr,Marc Zimmermannlations of transitivity, preference reversals, and violations of independence axioms first discussed by Allais and Ellsberg. The model is based on Fishburn’s skew-symmetric bilinear representation for preferences between lotteries and on two types of state parameters. One of these mimics the traditi
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https://doi.org/10.1007/978-3-662-67194-8y are identified uncertainty regarding the alternate courses of action, uncertainty about the outcomes of the action, and uncertainty about the decision maker’s preferences. A typology of decision methodologies based on various combinations of knowledge about the representation of preferences and un
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https://doi.org/10.1007/978-3-662-67194-8measure. Among other things, the Bayes formula, a theorems on a extension of a fuzzy P-measure, an integral of probability and a connections between a fuzzy P-measure and anothers measures are presented. Also, the theory of fuzzv random variables, adequate for fuzzy P-measures, is shown. Finally an
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https://doi.org/10.1007/978-3-642-37181-3problems arising in scientific investigations generate data incorporating non-statistical uncertainty. In such instances a fuzzy axiomatic structure for dealing with such problems usually increase both their mathematical tractability and physical realism. In this paper we deal with the extension of
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Kristin Eissfeldt,Christa Jaegerof the theory of expert systems, can be sometimes done within certain probabilistic framework. This type of reasoning can be also characterized as a suggestion to cope with the situation described by a large number of data by constructing a rough simplifying probabilistic model. Further as a byprodu
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