Peer review, evaluation, and selection is the foundation on which modern science is built. Funding bodies the world over employ experts to study and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more universal: a professional society may want give a subset of its members awards based on the opinions of all the members; an instructor for a MOOC or 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 procedures or mechanisms for peer selection, a specific type of group decision making problem studied in computer science, economics, political science, and beyond. First, we detail a novel mechanism that is strategyproof, i.e., agents cannot benefit themselves by reporting insincere valuations, in addition to other desirable normative properties. Second, we demonstrate the effectiveness of our mechanism through a comprehensive simulation based comparison of our mechanism with a suite of mechanisms found in the computer science and economics literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it can be used as a randomized method to addresses the ubiquitous apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided fairly.
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