Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning.

Abstract

Introduction: Traditional consensus-reaching processes in large-scale group decision making (LSGDM) rely heavily on empirically determined feedback parameters and have difficulty balancing consensus efficiency with the preservation of expert opinions. To address this problem, this paper proposes a reinforcement learning-based machine-assisted adaptive feedback consensus method.

Methods: First, a hesitation degree is introduced to improve the score function of hesitant fuzzy linguistic term sets (HFLTSs), thereby reducing information loss during linguistic information transformation. Second, a comprehensive relationship matrix integrating preference similarity and social trust relationships is constructed, and K-Means clustering is employed to divide large-scale expert groups. On this basis, the consensus-reaching process is modeled as a Markov decision process (MDP), and the LSGDM consensus-reaching process is modeled as a dynamic sequential decision problem. A deep deterministic policy gradient (DDPG) agent is utilized to learn feedback adjustment strategies, enabling feedback parameters to be dynamically adjusted according to the evolution state of group opinions. Meanwhile, network text data and the TF-IDF method are combined to determine attribute weights and improve decision objectivity. Finally, an emergency decision-making case of the "Beijing-Tianjin-Hebei rainstorm" is conducted to verify the proposed method.

Results: The results show that the proposed method can achieve the preset consensus threshold within fewer discussion rounds while effectively balancing the group consensus level and expert opinion retention.

Discussion/conclusion: These findings verify the effectiveness and feasibility of the proposed method, indicating that it can provide effective decision support for adaptive feedback consensus reaching in LSGDM emergency decision-making.