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Stochastic Dominance Decision Rules when the Attributes are Utility Independent

Author

Listed:
  • K. C. Mosler

    (Fachbereich Wirtschafts-und Organisationswissenschaften, Hochschule der Bundeswehr, Postfach 700822, D-2000 Hamburg, Federal Republic of Germany)

Abstract

In multivariate decisions under risk, assessing the complete utility function can be a major obstacle. Decision rules are investigated which characterize uniformly better alternatives with respect to a whole class of utility functions. In this paper independence assumptions are imposed on the preference structure while the levels of attributes may be stochastically dependent in an arbitrary way. The utilities considered are additive, multiplicative, or multilinear. Necessary and sufficient conditions are developed for uniform decisions over utilities with common substitutional structure and where the univariate conditional utilities show qualitative properties such as risk aversion. The rules are direct extensions of known univariate rules and easy to evaluate.

Suggested Citation

  • K. C. Mosler, 1984. "Stochastic Dominance Decision Rules when the Attributes are Utility Independent," Management Science, INFORMS, vol. 30(11), pages 1311-1322, November.
  • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:11:p:1311-1322
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    File URL: http://dx.doi.org/10.1287/mnsc.30.11.1311
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    Citations

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    Cited by:

    1. Range, Troels Martin & Østerdal, Lars Peter, 2013. "Checking bivariate first order dominance," Discussion Papers of Business and Economics 9/2013, University of Southern Denmark, Department of Business and Economics.
    2. O'Neill,Barry, 1998. "Risk Aversion in International Relations Theory," Discussion Paper Serie B 445, University of Bonn, Germany.
    3. Abdelaziz, Fouad Ben, 2012. "Solution approaches for the multiobjective stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 1-16.
    4. Østerdal, Lars Peter, 2010. "The mass transfer approach to multivariate discrete first order stochastic dominance: Direct proof and implications," Journal of Mathematical Economics, Elsevier, vol. 46(6), pages 1222-1228, November.
    5. Bell, Michelle L. & Hobbs, Benjamin F. & Ellis, Hugh, 2003. "The use of multi-criteria decision-making methods in the integrated assessment of climate change: implications for IA practitioners," Socio-Economic Planning Sciences, Elsevier, vol. 37(4), pages 289-316, December.
    6. Walter Krämer, 2016. "Walter Krämer: Interview mit Karl Mosler," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 63-71, February.
    7. F. Ben Abdelaziz & P. Lang & R. Nadeau, 1999. "Dominance and Efficiency in Multicriteria Decision under Uncertainty," Theory and Decision, Springer, vol. 47(3), pages 191-211, December.
    8. Abdelaziz, F. Ben & Lang, P. & Nadeau, R., 1995. "Distributional efficiency in multiobjective stochastic linear programming," European Journal of Operational Research, Elsevier, vol. 85(2), pages 399-415, September.
    9. Denuit, Michel M. & Mesfioui, Mhamed, 2017. "Preserving the Rothschild–Stiglitz type increase in risk with background risk: A characterization," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 1-5.
    10. Finkelshtain, Israel & Kella, Offer & Scarsini, Marco, 1999. "On risk aversion with two risks," Journal of Mathematical Economics, Elsevier, vol. 31(2), pages 239-250, March.

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