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Axiomatic Foundations of Generalized Qualitative Utility

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Book cover Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8271))

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Abstract

The aim of this paper is to provide a unifying axiomatic justification for a class of qualitative decision models comprising among others optimistic/pessimistic qualitative utilities, binary possibilistic utility, likelihood-based utility, Spohn’s disbelief function-based utility. All those criteria that are instances of Algebraic Expected Utility have been shown to be counterparts of Expected Utility thanks to a unifying axiomatization in a von Neumann-Morgenstern setting when non probabilistic decomposable uncertainty measures are used. Those criteria are based on ( ⊕ , ⊗ ) operators, counterpart of ( + , ×) used by Expected Utility, where ⊕ is an idempotent operator and ⊗ is a triangular norm. The axiomatization is lead in the Savage setting which is a more general setting than that of von Neumann-Morgenstern as here we do not assume that the uncertainty representation of the decision-maker is known.

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Author information

Authors and Affiliations

  1. LIP6, UPMC, France

    Paul Weng

Authors
  1. Paul Weng
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Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  2. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, Nova Scotia, Canada

    Pawan Lingras

  3. Faculty of Informatics, Mahasarakham University, Khamreang, 44150, Kantarawichai, Mahasarakham, Thailand

    Chattrakul Sombattheera

  4. Department of Computing, Curtin University, Perth, WA, Australia

    Aneesh Krishna

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Weng, P. (2013). Axiomatic Foundations of Generalized Qualitative Utility. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_28

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  • DOI: https://doi.org/10.1007/978-3-642-44949-9_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44948-2

  • Online ISBN: 978-3-642-44949-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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