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    This study introduces a social network method for large-scale group decision-making (LSGDM) that identifies and manages pseudo-trust behavior. It enhances decision efficiency and consensus by using feature selection and a novel consensus model.

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    Area of Science:

    • Artificial Intelligence
    • Decision Science
    • Social Network Analysis

    Background:

    • Group decision-making (GDM) is evolving into complex large-scale GDM (LSGDM) due to AI advancements.
    • Reaching consensus in LSGDM relies on decision-maker (DM) trust, but pseudo-trust is a prevalent issue.
    • Identifying and managing pseudo-trust behavior is crucial for effective LSGDM.

    Purpose of the Study:

    • To investigate the impact of pseudo-trust on DM dimensionality reduction and consensus.
    • To propose a social network LSGDM method incorporating feature selection and pseudo-trust management.
    • To develop a quantitative assessment system for pseudo-trust behavior.

    Main Methods:

    • A leader feature selection approach based on dual trust relationships for DM dimensionality reduction.
    • An adaptive consensus model designed for the identification and management of pseudo-trust behavior.
    • Validation using real-world case studies from the UCI database with experimental and comparative analyses.

    Main Results:

    • The proposed method effectively addresses challenges in LSGDM dimensionality reduction and consensus.
    • A clear concept and quantitative assessment system for pseudo-trust behavior were established.
    • The social network LSGDM method demonstrated effectiveness, practicability, and superiority over existing approaches.

    Conclusions:

    • Pseudo-trust significantly influences LSGDM processes, necessitating targeted management strategies.
    • The developed social network LSGDM method provides a robust framework for handling pseudo-trust.
    • This research offers a valuable contribution to improving the rationality and efficiency of large-scale group decision-making.