# Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise

arXiv: https://arxiv.org/abs/2201.10631 · HTML: https://mlsanity.com/paper/2201.10631/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 21 of 21)

- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [Design and Analysis of the NIPS 2016 Review Process](https://mlsanity.com/paper/1708.09794/) (2017)
- [PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review](https://mlsanity.com/paper/1806.06237/) (2018)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings](https://mlsanity.com/paper/1806.05085/) (2018)
- [On Strategyproof Conference Peer Review](https://mlsanity.com/paper/1806.06266/) (2018)
- [On Testing for Biases in Peer Review](https://mlsanity.com/paper/1912.13188/) (2019)
- [Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment](https://mlsanity.com/paper/2010.04041/) (2020)
- [Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment](https://mlsanity.com/paper/1604.03632/) (2016)
- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [Uncovering Latent Biases in Text: Method and Application to Peer Review](https://mlsanity.com/paper/2010.15300/) (2020)
- [Tuned Models of Peer Assessment in MOOCs](https://mlsanity.com/paper/1307.2579/) (2013)
- [Loss Functions, Axioms, and Peer Review](https://mlsanity.com/paper/1808.09057/) (2018)
- [A SUPER* Algorithm to Optimize Paper Bidding in Peer Review](https://mlsanity.com/paper/2007.07079/) (2020)
- [Debiasing Evaluations That are Biased by Evaluations](https://mlsanity.com/paper/2012.00714/) (2020)
- [PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection](https://mlsanity.com/paper/2004.14939/) (2020)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)
- [Optimal Impartial Selection](https://mlsanity.com/paper/1310.8631/) (2013)
- [A Near-Optimal Mechanism for Impartial Selection](https://mlsanity.com/paper/1407.8535/) (2014)
- [Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review](https://mlsanity.com/paper/2011.14646/) (2020)
- [Incentives, Quality, and Risks: A Look Into the NSF Proposal Review Pilot](https://mlsanity.com/paper/1307.6528/) (2013)

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

- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [Group Fairness in Peer Review](https://mlsanity.com/paper/2410.03474/) (2024)
- [Manipulation and Peer Mechanisms: A Survey](https://mlsanity.com/paper/2210.01984/) (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)

## Abstract (arXiv)

Strategic behavior is a fundamental problem in a variety of real-world applications that require some form of peer assessment, such as peer grading of homeworks, grant proposal review, conference peer review of scientific papers, and peer assessment of employees in organizations. Since an individual's own work is in competition with the submissions they are evaluating, they may provide dishonest evaluations to increase the relative standing of their own submission. This issue is typically addressed by partitioning the individuals and assigning them to evaluate the work of only those from different subsets. Although this method ensures strategyproofness, each submission may require a different type of expertise for effective evaluation. In this paper, we focus on finding an assignment of evaluators to submissions that maximizes assigned evaluators' expertise subject to the constraint of strategyproofness. We analyze the price of strategyproofness: that is, the amount of compromise on the assigned evaluators' expertise required in order to get strategyproofness. We establish several polynomial-time algorithms for strategyproof assignment along with assignment-quality guarantees. Finally, we evaluate the methods on a dataset from conference peer review.