# On Strategyproof Conference Peer Review

arXiv: https://arxiv.org/abs/1806.06266 · HTML: https://mlsanity.com/paper/1806.06266/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [A Dataset on Malicious Paper Bidding in Peer Review](https://mlsanity.com/paper/2207.02303/) (2022)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)
- [Combating Collusion Rings is Hard but Possible](https://mlsanity.com/paper/2112.08444/) (2021)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [Group Fairness in Peer Review](https://mlsanity.com/paper/2410.03474/) (2024)
- [exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem](https://mlsanity.com/paper/2502.07683/) (2025)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [Auctions and Peer Prediction for Academic Peer Review](https://mlsanity.com/paper/2109.00923/) (2021)
- [Manipulation and Peer Mechanisms: A Survey](https://mlsanity.com/paper/2210.01984/) (2022)
- [Improving Peer Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2111.04466/) (2021)
- [No Agreement Without Loss: Learning and Social Choice in Peer Review](https://mlsanity.com/paper/2211.02144/) (2022)
- [Eliciting Honest Information From Authors Using Sequential Review](https://mlsanity.com/paper/2311.14619/) (2023)
- [Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition](https://mlsanity.com/paper/2510.12692/) (2025)
- [Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy](https://mlsanity.com/paper/2512.17950/) (2025)
- [Reimagining Peer Review Process Through Multi-Agent Mechanism Design](https://mlsanity.com/paper/2601.19778/) (2026)
- [Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources](https://mlsanity.com/paper/2610.19038/) (2026)

## Abstract (arXiv)

We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors. This overlap can incentivize strategic reviews to influence the final ranking of one's own papers. In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review. We first present and analyze an algorithm for reviewer-assignment and aggregation that guarantees strategyproofness and a natural efficiency property called unanimity, when the authorship graph satisfies a simple property. Our algorithm is based on the so-called partitioning method, and can be thought as a generalization of this method to conference peer review settings. We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review. Finally, we complement our positive results with negative theoretical results where we prove that under various ways of strengthening the requirements, it is impossible for any algorithm to be strategyproof and efficient.