Trust-driven consensus reaching in human-AI hybrid large-scale group decision-making

To address the issues of insufficient trust representation, lack of feedback in opinion conflicts, and low consensus convergence efficiency in human-AI hybrid group decision-making, this paper proposes a trust-driven consensus-reaching method for human-AI collaborative decision-making.

Centered on trust modeling, the proposed method integrates human experts and large language models into a unified collaborative framework. By constructing dynamic trust relationships among multiple agents, it realizes the coupled evolution of trust mechanisms and opinion dynamics. Furthermore, a differentiated opinion updating mechanism is designed based on trust propagation, and combined with consensus measurement and feedback regulation to form an iterative process from initial opinions to a stable consensus solution.

Based on the Zhengzhou "7.20" rainstorm case, and in comparison with traditional weight adjustment methods and dynamic regulation methods based on deep reinforcement learning, the proposed method preliminarily validates its potential advantages in the above indicators.

Under the conditions of this case, the proposed method exhibits a trend of achieving higher consensus quality with lower intervention costs.