A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks.

Area of Science:

• Decision Sciences
• Artificial Intelligence
• Fuzzy Logic

Background:

• Conventional group decision making (GDM) often assumes independent experts, neglecting crucial interpersonal interactions.
• Expert opinions and influence can be significantly affected by communication, collaboration, and trust within a group.
• Efficient information transmission relies on trusted communication channels, a factor often overlooked in GDM.

Purpose of the Study:

• To develop an optimal trust-induced consensus process for GDM that incorporates expert interactions.
• To decompose expert weights into individual and interactive components to better reflect real-world dynamics.
• To enhance GDM by utilizing intuitionistic fuzzy preference relations and trust mechanisms.

Main Methods:

• Decomposition of expert weights into individual and interactive components.
• Construction of three optimization models for weight parameters using 2-order additive fuzzy measures and Shapley values.
• Application of the Choquet integral for opinion aggregation and a novel distance measure for consensus index calculation.

Main Results:

• A new method for calculating comprehensive expert weights that includes interactive effects.
• An effective trust-induced consensus process that improves upon traditional GDM approaches.
• Demonstrated effectiveness and improvements through an illustrative example and comparative analysis.

Conclusions:

• The proposed trust-induced consensus process effectively models expert interactions in GDM.
• Incorporating trust and interaction significantly enhances the consensus-building process.
• The method offers a more realistic and improved approach to GDM compared to conventional techniques.