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Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

Enrique Herrera-Viedma1, Francisco Chiclana, Francisco Herrera

  • 1Department of Computer Science and Artificial Intelligence, University of Granada, Spain. viedma@decsai.ugr.es

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2007
PubMed
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This study introduces a new model for group decision-making using incomplete fuzzy preference relations. It estimates missing expert preferences and aggregates them using a novel operator, prioritizing consistent information for better decision outcomes.

Area of Science:

  • Decision Sciences
  • Fuzzy Logic Systems
  • Operations Research

Background:

  • Experts may lack complete knowledge, leading to missing preferences in decision-making.
  • Incomplete fuzzy preference relations pose challenges for group decision-making models.
  • Existing methods may struggle to handle uncertainty and missing data effectively.

Purpose of the Study:

  • To develop a novel group decision-making model accommodating incomplete fuzzy preference relations.
  • To propose an iterative procedure for estimating missing preference values.
  • To introduce a new aggregation operator that prioritizes consistent expert opinions.

Main Methods:

  • Utilizing an iterative estimation procedure guided by the additive-consistency (AC) property.

Related Experiment Videos

  • Measuring expert information consistency using the AC property.
  • Developing an AC-IOWA operator for aggregating preferences, emphasizing consistency.
  • Employing quantifier-guided choice degrees (dominance and nondominance) for alternative selection.

    • Main Results:

    • Successfully estimated missing information in incomplete fuzzy preference relations.
    • Developed and validated the AC-IOWA operator for consistent preference aggregation.
    • Demonstrated a robust method for selecting alternatives based on fuzzy majority.

    Conclusions:

    • The proposed model effectively handles incomplete fuzzy preferences in group decision-making.
    • The AC-IOWA operator enhances aggregation by weighting consistent expert judgments.
    • The approach provides a reliable framework for decision-making under uncertainty.