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AI系统越来越多地介入协商过程，理解协商议题数量对人类表现的影响对于维护人类自主权至关重要。我们在真实租房场景下设计了一项人机协商案例研究，通过改变协商议题数量展开实验；实证结果表明，在没有辅助支持的情况下，当议题数量不超过三个时人类表现保持稳定，但随着议题增加带来的认知负荷上升，表现出现下降。为解决此问题，我们提出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。该方法展示了互可接受协议空间如何随协商进程推进而收窄，帮助用户识别有前景的选项。在组内实验（N=32）中，该方法改善了人类协商结果与效率，保持了人类控制权，并避免了价值重新分配。我们的发现揭示了人机协商中人类可应对复杂程度的实践极限，推进了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。