Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment
This work addresses peer selection for applications like funding reviews and crowdsourced grading, offering a strategyproof solution, though it appears incremental as it builds on existing mechanisms with a new randomized rounding technique.
The authors tackled the peer selection problem by proposing a novel mechanism that is strategyproof, preventing agents from benefiting from insincere reports, and demonstrated its effectiveness through comprehensive simulations comparing it to existing methods.
Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a Massive Open Online Course (MOOC) or an online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.