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A satisfactory-oriented approach to multiexpert decision-making with linguistic assessments
Van-Nam Huynh1, Yoshiteru Nakamori
1Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa 923-1292, Japan. huynh@jaist.ac.jp
Summary
This study introduces a novel multiexpert decision-making (MEDM) method using random preferences and a satisfactory principle for linguistic assessments. It offers a new approach to ranking alternatives in complex decision scenarios.
Area of Science:
- Decision Sciences
- Artificial Intelligence
- Computational Linguistics
Background:
- Traditional multiexpert decision-making (MEDM) involves aggregation and exploitation phases.
- Existing methods often rely on aggregation operators to combine expert preferences.
- Handling linguistic assessments in MEDM presents unique challenges.
Purpose of the Study:
- To propose a novel MEDM method incorporating linguistic assessments.
- To introduce the concepts of random preferences and a satisfactory principle.
- To develop a linguistic choice function for ranking alternatives.
Main Methods:
- Utilizing random preferences in the aggregation phase instead of traditional operators.
- Defining and applying a satisfactory principle for selecting the best alternative.
- Developing a linguistic choice function for rank ordering alternatives.
- Extending the method to multigranular linguistic contexts.
Main Results:
- The proposed method effectively ranks alternatives using linguistic assessments and a satisfactory principle.
- Demonstrated applicability in two literature-based examples.
- Provides a framework for MEDM in complex linguistic environments.
Conclusions:
- The novel MEDM method offers an effective alternative for decision-making with linguistic data.
- The satisfactory principle provides a robust criterion for selecting optimal alternatives.
- The approach is adaptable to various linguistic granularities.