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

Mehul Parmar; Chaklam Silpasuwanchai

doi:10.1145/3772318.3790358

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arXiv · 2603.22766

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

Mehul Parmar·Chaklam Silpasuwanchai

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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 for interactive systems.

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Mehul Parmar, Chaklam Silpasuwanchai. 2026-03-24. From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization. https://doi.org/10.1145/3772318.3790358

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