_:n00373e576bf14f13a3f2db32b9907694xb23
<http://schema.org/description>
"Peer review, evaluation, and selection is a fundamental aspect of modern\nscience. Funding bodies the world over employ experts to review and select the\nbest proposals of those submitted for funding. The problem of peer selection,\nhowever, is much more general: a professional society may want to give a subset\nof its members awards based on the opinions of all members; an instructor for a\nMOOC or online course may want to crowdsource grading; or a marketing company\nmay select ideas from group brainstorming sessions based on peer evaluation. We\nmake three fundamental contributions to the study of procedures or mechanisms\nfor peer selection, a specific type of group decision-making problem, studied\nin computer science, economics, and political science. First, we propose a\nnovel mechanism that is strategyproof, i.e., agents cannot benefit by reporting\ninsincere valuations. Second, we demonstrate the effectiveness of our mechanism\nby a comprehensive simulation-based comparison with a suite of mechanisms found\nin the literature. Finally, our mechanism employs a randomized rounding\ntechnique that is of independent interest, as it solves the apportionment\nproblem that arises in various settings where discrete resources such as\nparliamentary representation slots need to be divided proportionally."
<https://cdnjs.deepai.org/profile/jeffrey-s-rosenschein>