As AI systems increasingly mediate negotiations, understanding how the number of negotiated issues impacts human performance is crucial for maintaining human agency. We designed a human-AI negotiation case study in a realistic property rental scenario, varying the number of negotiated issues; empirical findings show that without support, performance stays stable up to three issues but declines as additional issues increase cognitive load. To address this, we introduce a novel uncertainty-based visualization driven by Bayesian estimation of agreement probability. It shows how the space of mutually acceptable agreements narrows as negotiation progresses, helping users identify promising options. In a within-subjects experiment (N=32), it improved human outcomes and efficiency, preserved human control, and avoided redistributing value. Our findings surface practical limits on the complexity people can manage in human-AI negotiation, advance theory on human performance in complex negotiations, and offer validated design guidance for interactive systems.

翻译：随着人工智能系统越来越多地介入谈判过程，理解议题数量如何影响人类表现对于维护人类自主性至关重要。我们设计了一个现实租赁场景下的人机协商案例研究，通过改变协商议题的数量；实验发现，在没有支持的情况下，当议题不超过三个时表现保持稳定，但随着议题增加导致认知负荷上升，表现开始下降。为解决这一问题，我们引入了一种基于贝叶斯协议概率估计的新型不确定性可视化方法。该方法展示了随着协商推进，双方可接受协议空间如何逐步收窄，从而帮助用户识别有利选项。在组内实验（N=32）中，该方法提升了用户协商结果和效率，保留了人类控制权，并避免了价值再分配。我们的发现揭示了人类在人机协商中可管理复杂性的实际限度，推进了关于复杂协商中人类表现的理论研究，并为交互系统提供了经过验证的设计指导。