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    This study introduces a novel multiattribute group decision-making (MAGDM) method using probabilistic linguistic information. It enhances group consensus and assessment aggregation, improving decision accuracy for complex problems.

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    Area of Science:

    • Decision Sciences
    • Artificial Intelligence
    • Operations Research

    Background:

    • Multiattribute group decision-making (MAGDM) often faces challenges with uncertain and linguistic information.
    • Existing methods may struggle with effectively allocating ignorance and achieving genuine group consensus.

    Purpose of the Study:

    • To propose a novel MAGDM method incorporating probabilistic linguistic information.
    • To address the allocation of ignorance, group consensus realization, and assessment aggregation.
    • To enhance decision-making accuracy in complex scenarios.

    Main Methods:

    • Developed an optimization model to allocate ignorance information by minimizing expert distances.
    • Defined a consensus index considering linguistic term (LT) information granules.
    • Employed particle swarm optimization (PSO) for adaptive consensus reaching (ACR).
    • Utilized the evidential reasoning (ER) algorithm for robust assessment aggregation, minimizing information loss.

    Main Results:

    • The proposed MAGDM method effectively handles probabilistic linguistic information.
    • The ACR model successfully optimizes LT information granules for consensus.
    • The ER algorithm provides accurate aggregation of assessments.
    • Demonstrated applicability and advantages through a financial technology company selection problem.

    Conclusions:

    • The developed MAGDM method offers a significant advancement in decision-making under uncertainty.
    • The integration of ACR and ER algorithms provides a powerful framework for group decisions.
    • The method shows superior performance compared to existing approaches.