Unfamiliar decisions -- decisions where people lack adequate domain knowledge or expertise -- specifically increase the complexity and uncertainty of the process of searching for, understanding, and making decisions with online information. Through our formative study (n=14), we observed users' challenges in accessing diverse perspectives, identifying relevant information, and deciding the right moment to make the final decision. We present ChoiceMates, a system that enables conversations with a dynamic set of LLM-powered agents for a holistic domain understanding and efficient discovery and management of information to make decisions. Agents, as opinionated personas, flexibly join the conversation, not only providing responses but also conversing among themselves to elicit each agent's preferences. Our between-subjects study (n=36) comparing ChoiceMates to conventional web search and single-agent showed that ChoiceMates was more helpful in discovering, diving deeper, and managing information compared to Web with higher confidence. We also describe how participants utilized multi-agent conversations in their decision-making process.

翻译：陌生决策——即人们在缺乏相关领域知识或专长时所做的决策——尤其会增加在线信息检索、理解与决策过程的复杂性和不确定性。通过形成性研究（n=14），我们观察到用户在获取多元视角、识别相关信息以及把握最终决策最佳时机方面面临的挑战。我们提出ChoiceMates系统，该系统支持与动态配置的LLM驱动智能体进行对话，以实现对领域的整体理解，并高效发现与管理决策所需信息。作为具有特定立场的角色，智能体可灵活加入对话，不仅提供应答，还能相互讨论以阐明各自偏好。通过组间实验（n=36）对比ChoiceMates与传统网页搜索及单智能体系统，结果表明：与网页搜索相比，ChoiceMates在信息发现、深度挖掘与管理方面更具助益性，且用户决策自信度更高。我们还描述了参与者如何在决策过程中利用多智能体对话。