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

arXiv: https://arxiv.org/abs/2603.22766 · HTML: https://mlsanity.com/paper/2603.22766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour](https://mlsanity.com/paper/2311.09410/) (2023)
- [Evaluating Language Model Agency through Negotiations](https://mlsanity.com/paper/2401.04536/) (2024)
- [Let's Negotiate! A Survey of Negotiation Dialogue Systems](https://mlsanity.com/paper/2212.09072/) (2022)
- [Decision-Oriented Dialogue for Human-AI Collaboration](https://mlsanity.com/paper/2305.20076/) (2023)
- [UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library](https://mlsanity.com/paper/2202.09895/) (2022)
- [Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach](https://mlsanity.com/paper/2208.11660/) (2022)
- [It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games](https://mlsanity.com/paper/2311.08666/) (2023)
- [Unpacking Human-AI interactions: From interaction primitives to a design space](https://mlsanity.com/paper/2401.05115/) (2024)
- ["ChatGPT, Don't Tell Me What to Do": Designing AI for Context Analysis in Humanitarian Frontline Negotiations](https://mlsanity.com/paper/2410.09139/) (2024)
- [A Bayesian Cognition Approach to Improve Data Visualization](https://mlsanity.com/paper/1901.02949/) (2019)
- [A Probabilistic Interpretation of Motion Correlation Selection Techniques](https://mlsanity.com/paper/2102.03955/) (2021)

## Abstract (arXiv)

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.