用贝叶斯可视化帮人应对多议题人机谈判，提升决策效率
From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization
- 基于贝叶斯估计动态展示可接受协议空间的缩小过程
- 32人实验显示：该工具显著提升谈判结果与效率
- 适合需要保持人类主导权的复杂协商场景
随着AI在谈判中扮演越来越重要的角色，理解议题数量对人类表现的影响至关重要。我们设计了一个真实的房屋租赁谈判案例，变化协商议题数量；实证发现，在无支持情况下，人类表现维持稳定最多三个议题，但随着议题增加，认知负荷上升导致表现下降。为此，我们提出一种基于不确定性的新型可视化方法，利用贝叶斯估计动态呈现协议达成概率，直观展示可接受协议空间随谈判进展逐渐缩小，帮助用户识别有潜力的选项。在一项包含32名参与者的一致性实验中，该方法显著提升了人类谈判结果与效率，保持了人类控制力，且未改变价值分配。研究揭示了人在人机谈判中可处理复杂度的实际限制，推动了复杂谈判中人类表现理论的发展，并为交互系统设计提供了验证过的指导。
原文摘要 · Abstract (English)
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.
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