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Partial Preference Information Methods Exploring the Space of Weights

In: Multi-Criteria Decision Making with Partial Preference Information

Author

Listed:
  • Adiel Teixeira de Almeida

    (Federal University of Pernambuco)

  • Eduarda Asfora Frej

    (Federal University of Pernambuco)

  • Lucia Reis Peixoto Roselli

    (Federal University of Pernambuco)

  • Jonatas Araújo de Almeida

    (Federal University of Pernambuco)

  • Ana Paula Cabral Seixas Costa

    (Federal University of Pernambuco)

  • Danielle Costa Morais

    (Federal University of Pernambuco)

Abstract

This chapter presents an overview of MCDM/A methods that work with partial information about the Decision Maker’s (DM’s) preferences, based on an exploration of the space of weights, i.e., the set of possible vectors of weights compatible with the DM’s judgments. The space of weights is defined based on the set of inequalities/equations derived from the partial preference information provided by the DM. There are different possibilities for exploring the weights space with a view to deriving a recommendation for the DM, such as: using mathematical programming models to obtain either dominance relations or to analyze potential optimality; applying decision rules or even working based on simulation-based techniques to derive recommendation based on robustness analysis. First, an overview of these different possibilities for dealing with the space of weights is discussed in general. Then, an overview is presented of some methods in the literature that work based on these approaches.

Suggested Citation

  • Adiel Teixeira de Almeida & Eduarda Asfora Frej & Lucia Reis Peixoto Roselli & Jonatas Araújo de Almeida & Ana Paula Cabral Seixas Costa & Danielle Costa Morais, 2026. "Partial Preference Information Methods Exploring the Space of Weights," International Series in Operations Research & Management Science, in: Multi-Criteria Decision Making with Partial Preference Information, chapter 6, pages 91-108, Springer.
    • Handle: RePEc:spr:isochp:978-3-032-19284-4_6
    DOI: 10.1007/978-3-032-19284-4_6
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