Auctions and Prediction Markets for Scientific Peer Review

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. We present a two-stage mechanism which ties together the paper submission and review process, simultaneously incentivizing high-quality reviews and high-quality submissions. In the first 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 second stage, we propose a novel prediction market-style 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 by the Stage I auction is used in Stage II to pay reviewers based on the quality of their reviews.