[2603.22766] From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization

Computer Science > Human-Computer Interaction arXiv:2603.22766 (cs) [Submitted on 24 Mar 2026] Title:From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization Authors:Mehul Parmar, Chaklam Silpasuwanchai View a PDF of the paper titled From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization, by Mehul Parmar and 1 other authors View PDF HTML (experimental) Abstract: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 ...