# The PeerRank Method for Peer Assessment

arXiv: https://arxiv.org/abs/1405.7192 · HTML: https://mlsanity.com/paper/1405.7192/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2014.

## Built on by (most cited first, 7 of 7)

- [PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations](https://mlsanity.com/paper/2307.02762/) (2023)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [The Meta-Turing Test](https://mlsanity.com/paper/2205.05268/) (2022)
- [Better Peer Grading through Bayesian Inference](https://mlsanity.com/paper/2209.01242/) (2022)
- [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)
- [Mechanisms that play a game, not toss a coin](https://mlsanity.com/paper/2308.10413/) (2023)

## Abstract (arXiv)

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages. We identify some formal properties of the PeerRank method (for example, it satisfies axioms of unanimity, no dummy, no discrimination and symmetry), discuss some examples, compare with related work and evaluate the performance on some synthetic data. Our results show considerable promise, reducing the error in grade predictions by a factor of 2 or more in many cases over the natural baseline of averaging peer grades.