Auctions and Peer Prediction for Academic Peer Review
This addresses inefficiencies in peer review for academic communities, offering a novel incentive-based solution.
The paper tackles the problems of high reviewer demand and poor incentives in academic peer review by proposing a mechanism that combines a VCG auction for paper submissions with a novel peer prediction method (H-DIPP) to incentivize high-quality reviews, using auction revenue to pay reviewers.
Peer reviewed publications are considered the gold standard in certifying and disseminating ideas that a research community considers valuable. However, we identify two major drawbacks of the current system: (1) the overwhelming demand for reviewers due to a large volume of submissions, and (2) the lack of incentives for reviewers to participate and expend the necessary effort to provide high-quality reviews. In this work, we adopt a mechanism-design approach to propose improvements to the peer review process, tying together the paper submission and review processes and simultaneously incentivizing high-quality submissions and reviews. In the submission stage, authors participate in a VCG auction for review slots by submitting their papers along with a bid that represents their expected value for having their paper reviewed. For the reviewing stage, we propose a novel peer prediction mechanism (H-DIPP) building on recent work in the information elicitation literature, which incentivizes participating reviewers to provide honest and effortful reviews. The revenue raised in the submission stage auction is used to pay reviewers based on the quality of their reviews in the reviewing stage.