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Comparison of Risk Averse Utility Functions on Two-Dimensional Regions

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Modeling Decisions for Artificial Intelligence (MDAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10571))

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Abstract

Weighted quasi-arithmetic means on two-dimensional regions are demonstrated, and risk averse conditions are discussed by the corresponding utility functions. For two utility functions on two-dimensional regions, we introduce a concept that decision making with one utility is more risk averse than decision making with the other utility. A necessary condition and a sufficient condition for the concept are demonstrated by their utility functions. Several examples are given to explain them.

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Acknowledgments

This research is supported from JSPS KAKENHI Grant Number JP 16K05282.

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Authors and Affiliations

  1. Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu, 802-8577, Japan

    Yuji Yoshida

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  1. Yuji Yoshida
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Correspondence to Yuji Yoshida .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. Toho Gakuen, Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Kyushu Institute of Technology, Iizuka, Fukuoka, Japan

    Aoi Honda

  4. Kyushu Institute of Technology, Kitakyushu-shi, Fukuoka, Japan

    Sozo Inoue

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Yoshida, Y. (2017). Comparison of Risk Averse Utility Functions on Two-Dimensional Regions. In: Torra, V., Narukawa, Y., Honda, A., Inoue, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2017. Lecture Notes in Computer Science(), vol 10571. Springer, Cham. https://doi.org/10.1007/978-3-319-67422-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-67422-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67421-6

  • Online ISBN: 978-3-319-67422-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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