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Weighted Quasi-Arithmetic Means on Two-Dimensional Regions: An Independent Case

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Modeling Decisions for Artificial Intelligence (MDAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9880))

Abstract

Weighted quasi-arithmetic means on two-dimensional regions when weighting functions are independent and utility functions have independent forms are introduced, and some conditions on weighting functions are discussed to characterize the properties. The first-order stochastic dominance and risk premiums on two-dimensional regions are demonstrated. Several examples of two-dimensional utility functions are given by one-dimensional utility functions to explain main results.

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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 Kövde , Skövde, Sweden

    Vicenç Torra

  2. Toho Gakuen , Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Enginyeria de la Informacio I de les Com, Univ Autonoma de Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  4. Universitat d'Andorra , Sant Julià de Lòria, Andorra

    Cristina Yañez

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Yoshida, Y. (2016). Weighted Quasi-Arithmetic Means on Two-Dimensional Regions: An Independent Case. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-45656-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45655-3

  • Online ISBN: 978-3-319-45656-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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