A Consensus Method Based on Reinforcement Learning for Group Decision-Making

In group decision-making (GDM), the consensus-reaching process (CRP) is essential for aligning the diverse opinions of decision-makers (DMs) to achieve collective agreements. However, the process often faces obstacles due to the uncertainty of DMs in terms of unit cost and willingness to adjust opinions. To this end, this study constructs a new GDM framework by introducing reinforcement learning (RL) to the CRP. In this framework, we design a unit cost learning algorithm based on RL. The algorithm introduces an action space based on linguistic expressions, and therefore exhibits strong interpretability. On this basis, a weight reward-penalty mechanism based on asymmetric Nash bargaining is further proposed. The mechanism takes marginal and adjustment contributions as objective criteria, which provides a reasonable basis for improving consensus outcomes and managing non-cooperative behaviors. The proposed model incorporates both interactive and automatic strategies: the former is able to accurately capture individuals' willingness to cooperate with the help of RL, and the latter relies on optimization models to effectively reduce the time and cost spent on reaching consensus. Finally, we provide an example to illustrate the proposed approach and experimentally verify its feasibility and the potential of the RL framework.