2026 Volume 38 Issue 2 Pages 633-645
In decision-making within small groups, individual members are afforded the opportunity to freely articulate their opinions, which can foster both compromise and the emergence of novel ideas. However, previous studies have not sufficiently examined how such emergent interactions influence consensus formation and individual satisfaction. In the present study, we develop a reinforcement learning model that integrates individual satisfaction as a reward metric, and we employ simulation experiments to investigate the impact of agents generating new opinions during discussions on both consensus formation and satisfaction. The simulation results suggest that the introduction of new opinions not only enhances individual satisfaction but also reduces the cost—measured in the number of steps required—to achieve consensus. Moreover, we conduct a linguistic simulation using a large-scale language model (LLM) to assess the applicability of this approach to real-world contexts and to compare decision-making processes across different group sizes. Our findings indicate that, in small groups, agents tend to conclude discussions with uniformly high satisfaction levels and rapid convergence, whereas in larger groups significant divergence in satisfaction and opinion is observed, complicating the consensus process. Additionally, differences in the vocabulary and structural characteristics of decisions emerge, highlighting the importance of carefully designing group size and composition for complex decision-making tasks.
