Optimization of emergent opinions and decision-making processes in small groups 
through reinforcement learning

Shiiku, Shota; Takeuchi, Yugo

doi:10.11517/pjsai.JSAI2024.0_4R3OS8b04

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Optimization of emergent opinions and decision-making processes in small groups through reinforcement learning

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Shiiku, Shota
Shizuoka University;
Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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Citation

Shiiku, S., & Takeuchi, Y. (2024). Optimization of emergent opinions and decision-making processes in small groups through reinforcement learning. In The Japanese Society for Artificial Intelligence (Ed.), Proceedings of the Annual Conference of JSAI (pp. 1-4). Tokyo: J-STAGE. doi:10.11517/pjsai.JSAI2024.0_4R3OS8b04.

Cite as: https://hdl.handle.net/21.11116/0000-000F-E9E0-D

Abstract

In the decision-making process within small groups, members often have numerous opportunities to explicitly
express their own opinions and thoughts. This contrasts with larger groups, where decisions are more frequently
made by majority vote or delegated to specific individuals. As a result, small groups may exhibit more complex and
emergent interactions, such as compromises among members and the proposal of new ideas. However, these creative
aspects of interaction, commonly observed in small groups, have not received much attention to date. Therefore,
predicting how members of a small group will react or behave towards the conclusions reached after decision-making
is challenging. This study constructs a reinforcement learning model that incorporates the satisfaction of individual
members with the interactions and outcomes during the decision-making process of small groups. It reveals that
this approach leads to members positively accepting the conclusions reached by the group while also reducing the
time required to reach these decisions during the decision-making process.