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Artificial Intelligence and Decision Making, 2019, Issue 1, Pages 16–26
DOI: https://doi.org/10.14357/20718594190102
(Mi iipr158)
 

This article is cited in 1 scientific paper (total in 1 paper)

Decision analysis

Comparing poly-interval alternatives: “mean-risk” method

G. I. Chepelev

Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
Full-text PDF (426 kB) Citations (1)
Abstract: For poly-interval objects, such as generalized interval estimations and fuzzy values, are proposed methods to calculate numerical characteristics, which are similar to characteristics of distribution functions of probability theory (mathematical expectation, variance, average semideviation). The methods are based on the defuzzification of interval estimates of the indicated numerical characteristics in the case of fuzzy poly-interval objects and on the representation of generalized interval estimations as a probability mixture of distributions forming such generalized estimations. These results allow extend the well-known “mean-risk” method, which is usually used for comparing mono-interval values on their preference and risk, to the case of poly-interval estimations.
Keywords: comparing poly-interval alternatives, “mean-risk” method, generalized interval estimations, numerical characteristics of fuzzy poly-interval values.
Funding agency Grant number
Russian Foundation for Basic Research 18-07-00280
16-29-12864
17-07-00512
17-29-07021
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: G. I. Chepelev, “Comparing poly-interval alternatives: “mean-risk” method”, Artificial Intelligence and Decision Making, 2019, no. 1, 16–26
Citation in format AMSBIB
\Bibitem{Che19}
\by G.~I.~Chepelev
\paper Comparing poly-interval alternatives: ``mean-risk'' method
\jour Artificial Intelligence and Decision Making
\yr 2019
\issue 1
\pages 16--26
\mathnet{http://mi.mathnet.ru/iipr158}
\crossref{https://doi.org/10.14357/20718594190102}
\elib{https://elibrary.ru/item.asp?id=37179699}
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  • https://www.mathnet.ru/eng/iipr/y2019/i1/p16
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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