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Decision making under uncertainty with unknown utility function and rank-ordered probabilities

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  • Podinovski, Vladislav V.

Abstract

We consider the ranking of decision alternatives in decision analysis problems under uncertainty, under very weak assumptions about the type of utility function and information about the probabilities of the states of nature. Namely, the following two assumptions are required for the suggested method: the utility function is in the class of increasing continuous functions, and the probabilities of the states of nature are rank-ordered. We develop a simple analytical method for the partial ranking of decision alternatives under the stated assumptions. This method does not require solving optimization programs and is free of the rounding errors.

Suggested Citation

  • Podinovski, Vladislav V., 2014. "Decision making under uncertainty with unknown utility function and rank-ordered probabilities," European Journal of Operational Research, Elsevier, vol. 239(2), pages 537-541.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:537-541
    DOI: 10.1016/j.ejor.2014.05.023
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    References listed on IDEAS

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    Cited by:

    1. Baudry, Gino & Macharis, Cathy & Vallée, Thomas, 2018. "Range-based Multi-Actor Multi-Criteria Analysis: A combined method of Multi-Actor Multi-Criteria Analysis and Monte Carlo simulation to support participatory decision making under uncertainty," European Journal of Operational Research, Elsevier, vol. 264(1), pages 257-269.
    2. Post, Thierry, 2016. "Standard Stochastic Dominance," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1009-1020.
    3. Fang, Yi & Post, Thierry, 2017. "Higher-degree stochastic dominance optimality and efficiency," European Journal of Operational Research, Elsevier, vol. 261(3), pages 984-993.

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