# Manipulation and Peer Mechanisms: A Survey

arXiv: https://arxiv.org/abs/2210.01984 · HTML: https://mlsanity.com/paper/2210.01984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 92 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [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)
- [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)
- [Tuned Models of Peer Assessment in MOOCs](https://mlsanity.com/paper/1307.2579/) (2013)
- [Learning to Mitigate AI Collusion on Economic Platforms](https://mlsanity.com/paper/2202.07106/) (2022)
- [The PeerRank Method for Peer Assessment](https://mlsanity.com/paper/1405.7192/) (2014)
- [PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection](https://mlsanity.com/paper/2004.14939/) (2020)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [Optimal Impartial Selection](https://mlsanity.com/paper/1310.8631/) (2013)
- [A Near-Optimal Mechanism for Impartial Selection](https://mlsanity.com/paper/1407.8535/) (2014)
- [How effective can simple ordinal peer grading be?](https://mlsanity.com/paper/1602.07985/) (2016)
- [Mechanism Design Powered by Social Interactions](https://mlsanity.com/paper/2102.10347/) (2021)
- [Incentive-Compatible Diffusion](https://mlsanity.com/paper/1805.08013/) (2018)
- [Removing Bias and Incentivizing Precision in Peer-grading](https://mlsanity.com/paper/1807.11657/) (2018)
- [Impartial Selection with Additive Approximation Guarantees](https://mlsanity.com/paper/1910.00135/) (2019)
- [From Matching with Diversity Constraints to Matching with Regional Quotas](https://mlsanity.com/paper/2002.06748/) (2020)
- [Incentive-Compatible Selection Mechanisms for Forests](https://mlsanity.com/paper/2006.00600/) (2020)
- [Impartial selection with prior information](https://mlsanity.com/paper/2102.09002/) (2021)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [Impartial Selection with Additive Guarantees via Iterated Deletion](https://mlsanity.com/paper/2205.08979/) (2022)
- [Friend-Based Ranking](https://mlsanity.com/paper/1807.05093/) (2018)
- [Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment](https://mlsanity.com/paper/1908.01718/) (2019)
- [Incentive-Compatible Classification](https://mlsanity.com/paper/1911.08849/) (2019)
- [Incentive Compatible Mechanism for Influential Agent Selection](https://mlsanity.com/paper/2107.10612/) (2021)
- [Better Peer Grading through Bayesian Inference](https://mlsanity.com/paper/2209.01242/) (2022)

## Built on by (most cited first, 1 of 1)

- [Group Fairness in Peer Review](https://mlsanity.com/paper/2410.03474/) (2024)

## Abstract (arXiv)

In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges.