# PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection

arXiv: https://arxiv.org/abs/2004.14939 · HTML: https://mlsanity.com/paper/2004.14939/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism](https://mlsanity.com/paper/2110.14802/) (2021)
- [The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review](https://mlsanity.com/paper/2408.13430/) (2024)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review](https://mlsanity.com/paper/2510.02143/) (2025)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [Manipulation and Peer Mechanisms: A Survey](https://mlsanity.com/paper/2210.01984/) (2022)

## Abstract (arXiv)

In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics.