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Maximum likelihood solutions for multicriterial choice problems

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  • Podinovski, Vladislav V.

Abstract

We introduce the notion of a maximum likelihood optimal decision alternative for the choice problem with a finite set of decision alternatives, assuming a general parametric preference model of the decision maker. We also develop an optimization-based method for the identification of such alternative for the cases in which the parametric preference model is based on uncertain intervals for criterion trade-offs. The suggested approach can be seen as generalising stochastic multicriteria acceptability analysis (SMAA) to a wider modelling setting. It also provides a maximum likelihood interpretation of the SMAA acceptability index.

Suggested Citation

  • Podinovski, Vladislav V., 2020. "Maximum likelihood solutions for multicriterial choice problems," European Journal of Operational Research, Elsevier, vol. 286(1), pages 299-308.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:1:p:299-308
    DOI: 10.1016/j.ejor.2020.03.028
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    1. Natalia Novikova & Irina Pospelova, 2022. "Germeier’s Scalarization for Approximating Solution of Multicriteria Matrix Games," Mathematics, MDPI, vol. 11(1), pages 1-28, December.

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