{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-peerrank-method-for-peer-assessment","title":"The PeerRank Method for Peer Assessment","arxiv_id":"1405.7192","date":"2014-05-28","proceeding":null,"authors":["Toby Walsh"],"abstract":"We propose the PeerRank method for peer assessment. This constructs a grade\nfor an agent based on the grades proposed by the agents evaluating the agent.\nSince the grade of an agent is a measure of their ability to grade correctly,\nthe PeerRank method weights grades by the grades of the grading agent. The\nPeerRank method also provides an incentive for agents to grade correctly. As\nthe grades of an agent depend on the grades of the grading agents, and as these\ngrades themselves depend on the grades of other agents, we define the PeerRank\nmethod by a fixed point equation similar to the PageRank method for ranking\nweb-pages. We identify some formal properties of the PeerRank method (for\nexample, it satisfies axioms of unanimity, no dummy, no discrimination and\nsymmetry), discuss some examples, compare with related work and evaluate the\nperformance on some synthetic data. Our results show considerable promise,\nreducing the error in grade predictions by a factor of 2 or more in many cases\nover the natural baseline of averaging peer grades.","url_abs":"http://arxiv.org/abs/1405.7192v1","url_pdf":"http://arxiv.org/pdf/1405.7192v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-peerrank-method-for-peer-assessment","repo_url":"https://github.com/naman-ali/GCN-SOAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}