{"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/strategyproof-peer-selection-using","title":"Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment","arxiv_id":"1604.03632","date":"2016-04-13","proceeding":null,"authors":["Haris Aziz","Omer Lev","Nicholas Mattei","Jeffrey S. Rosenschein","Toby Walsh"],"abstract":"Peer reviews, evaluations, and selections are a fundamental aspect of modern\nscience. Funding bodies the world over employ experts to review and select the\nbest proposals from those submitted for funding. The problem of peer selection,\nhowever, is much more general: a professional society may want to give a subset\nof its members awards based on the opinions of all members; an instructor for a\nMassive Open Online Course (MOOC) or an online course may want to crowdsource\ngrading; or a marketing company may select ideas from group brainstorming\nsessions based on peer evaluation.\n  We make three fundamental contributions to the study of peer selection, a\nspecific type of group decision-making problem, studied in computer science,\neconomics, and political science. First, we propose a novel mechanism that is\nstrategyproof, i.e., agents cannot benefit by reporting insincere valuations.\nSecond, we demonstrate the effectiveness of our mechanism by a comprehensive\nsimulation-based comparison with a suite of mechanisms found in the literature.\nFinally, our mechanism employs a randomized rounding technique that is of\nindependent interest, as it solves the apportionment problem that arises in\nvarious settings where discrete resources such as parliamentary representation\nslots need to be divided proportionally.","url_abs":"http://arxiv.org/abs/1604.03632v4","url_pdf":"http://arxiv.org/pdf/1604.03632v4.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":"strategyproof-peer-selection-using","repo_url":"https://github.com/nmattei/peerselection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"marketing","task_name":"Marketing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1604.03632","atlas_url":"https://app.syntology.ai/?focus=1604.03632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.03632"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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