Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making

Area of Science:

• Decision Science
• Linguistics
• Artificial Intelligence

Background:

• Linguistic decision making relies on consistency measures.
• Current methods overlook personalized individual semantics (PISs), where word meanings vary among decision-makers.
• This omission impacts the accuracy of group decision-making outcomes.

Purpose of the Study:

• To propose a novel approach for improving consistency in linguistic group decision making.
• To integrate personalized individual semantics (PISs) into consistency measurement and improvement.
• To address the limitations of existing methods that ignore semantic variations.

Main Methods:

• Developed a novel consistency improving approach for linguistic group decision making.
• Incorporated personalized representation characteristics.
• Integrated a PIS-based model for measuring and enhancing linguistic preference relations.

Main Results:

• The proposed approach effectively measures and improves consistency in linguistic preference relations.
• Demonstrated the feasibility and effectiveness through detailed numerical and comparative analyses.
• Showcased the importance of considering PISs for accurate decision making.

Conclusions:

• The novel approach enhances linguistic decision-making consistency by incorporating personalized individual semantics (PISs).
• This method offers a more realistic and accurate way to handle linguistic preferences in group settings.
• Future research can build upon this PIS-based model for more robust decision support systems.