# Avoiding a Tragedy of the Commons in the Peer Review Process

arXiv: https://arxiv.org/abs/1901.06246 · HTML: https://mlsanity.com/paper/1901.06246/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

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

- [Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards](https://mlsanity.com/paper/2505.04966/) (2025)
- [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)
- [Some Ethical Issues in the Review Process of Machine Learning Conferences](https://mlsanity.com/paper/2106.00810/) (2021)
- [Questionable practices in machine learning](https://mlsanity.com/paper/2407.12220/) (2024)
- [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)
- [Auctions and Peer Prediction for Academic Peer Review](https://mlsanity.com/paper/2109.00923/) (2021)
- [Strengthening Subcommunities: Towards Sustainable Growth in AI Research](https://mlsanity.com/paper/2204.08377/) (2022)
- [Eliciting Honest Information From Authors Using Sequential Review](https://mlsanity.com/paper/2311.14619/) (2023)
- [How Much Were You Told? Measuring External Information in Peer Reviews](https://mlsanity.com/paper/2609.28041/) (2026)

## Abstract (arXiv)

Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.