# Mechanism Design with Predictions

arXiv: https://arxiv.org/abs/2205.11313 · HTML: https://mlsanity.com/paper/2205.11313/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2022.

## Built on by (most cited first, 7 of 7)

- [Mechanism Design With Predictions for Obnoxious Facility Location](https://mlsanity.com/paper/2212.09521/) (2022)
- [Learning-Augmented Robust Algorithmic Recourse](https://mlsanity.com/paper/2410.01580/) (2024)
- [Scheduling with Predictions](https://mlsanity.com/paper/2212.10433/) (2022)
- [Learning-Augmented Streaming Algorithms for Correlation Clustering](https://mlsanity.com/paper/2510.10705/) (2025)
- [On multiagent online problems with predictions](https://mlsanity.com/paper/2507.12486/) (2025)
- [Mechanism Design for Facility Location using Predictions](https://mlsanity.com/paper/2508.03818/) (2025)
- [Impartial Selection with Predictions](https://mlsanity.com/paper/2510.19002/) (2025)

## Abstract (arXiv)

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.