TY - GEN
T1 - A Market-Inspired Bidding Scheme for Peer Review Paper Assignment
AU - Meir, Reshef
AU - Lang, Jérôme
AU - Lesca, Julien
AU - Mattei, Nicholas
AU - Kaminsky, Natan
N1 - Publisher Copyright: Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85111216987&partnerID=8YFLogxK
U2 - 10.1609/aaai.v35i6.16609
DO - 10.1609/aaai.v35i6.16609
M3 - منشور من مؤتمر
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 4776
EP - 4784
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -