A Market-Inspired Bidding Scheme for Peer Review Paper Assignment

Reshef Meir, Jérôme Lang, Julien Lesca, Nicholas Mattei, Natan Kaminsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. We provide an analysis of the incentives of reviewers during the bidding phase, when reviewers have both private costs and some information about the demand for each paper; and their goal is to obtain the best possible k papers for a predetermined k. We show that by assigning ‘budgets’ to reviewers and a ‘price’ for every paper that is (roughly) proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. This game-theoretic analysis is based on a simple, prototypical assignment algorithm. We show via extensive simulations on bidding data from real conferences, that our bidding scheme would substantially improve both the bid distribution and the resulting assignment.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
Pages4776-4784
Number of pages9
ISBN (Electronic)9781713835974
DOIs
StatePublished - 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume6A

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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