From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process. Our user evaluation (n=12) shows that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality and confidence than a commercial multi-agent framework. This work provides insights into designing a more controllable and collaborative multi-agent system.
@article{arxiv.2310.01331,
title = {ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions},
author = {Jeongeon Park and Bryan Min and Kihoon Son and Jean Y. Song and Xiaojuan Ma and Juho Kim},
journal= {arXiv preprint arXiv:2310.01331},
year = {2026}
}