Exploring the Mutual Adaptation of Large Language Models and Emergent Decision-Making in Simulated Small Group Interactions

As Large Language Model (LLM) become more prevalent and capable, opportunities for influencing human decision-making are increasing. We engage in small groups, and complex and emergent interactions, such as compromises by members and proposals of new ideas, may occur. However, these creative aspects of interaction systems, frequently observed in small groups, have not received much attention. This study focuses on the decision-making process in small groups, considering individual members’ satisfaction with the interaction process and its outcome as variables. These variables were incorporated into the reward system of a reinforcement learning model, and learning was conducted. Furthermore, a linguistic simulation was performed using an LLM. The results suggest that when each member accepts the conclusion reached by the group with a positive attitude, it simultaneously reduces the time required to conclude decision-making. Moreover, the LLM can play a role in promoting the emergence of creative aspects in group decision-making, such as compromises and the proposal of new ideas. The findings highlight the potential of LLM in facilitating emergent decision-making processes in small groups and the importance of considering member satisfaction in enhancing the efficiency and creativity of group decision-making.