# A Bandit Model for Human-Machine Decision Making with Private Information and Opacity

arXiv: https://arxiv.org/abs/2007.04800 · HTML: https://mlsanity.com/paper/2007.04800/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Learning Personalized Decision Support Policies](https://mlsanity.com/paper/2304.06701/) (2023)
- [Capturing Humans' Mental Models of AI: An Item Response Theory Approach](https://mlsanity.com/paper/2305.09064/) (2023)
- [AI-Assisted Decision Making with Human Learning](https://mlsanity.com/paper/2502.13062/) (2025)
- [Online Decision Deferral under Budget Constraints](https://mlsanity.com/paper/2409.20489/) (2024)
- [When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making](https://mlsanity.com/paper/2308.11721/) (2023)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [HR-Bandit: Human-AI Collaborated Linear Recourse Bandit](https://mlsanity.com/paper/2410.14640/) (2024)
- [Strategic Decision Support for AI Agents](https://mlsanity.com/paper/2606.12587/) (2026)
- [LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning](https://mlsanity.com/paper/2601.11905/) (2026)
- [Authority-Preserving Evaluation of Medical Vision-Language Assistants](https://mlsanity.com/paper/2609.22302/) (2026)

## Abstract (arXiv)

Applications of machine learning inform human decision makers in a broad range of tasks. The resulting problem is usually formulated in terms of a single decision maker. We argue that it should rather be described as a two-player learning problem where one player is the machine and the other the human. While both players try to optimize the final decision, the setup is often characterized by (1) the presence of private information and (2) opacity, that is imperfect understanding between the decision makers. We prove that both properties can complicate decision making considerably. A lower bound quantifies the worst-case hardness of optimally advising a decision maker who is opaque or has access to private information. An upper bound shows that a simple coordination strategy is nearly minimax optimal. More efficient learning is possible under certain assumptions on the problem, for example that both players learn to take actions independently. Such assumptions are implicit in existing literature, for example in medical applications of machine learning, but have not been described or justified theoretically.