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In peer selection agents must choose a subset of themselves for an award or aprize. As agents are self-interested, we want to design algorithms that areimpartial, so that an individual agent cannot affect their own chance of beingselected. This problem has broad application in resource allocation andmechanism design and has received substantial attention in the artificialintelligence literature. Here, we present a novel algorithm for impartial peerselection, PeerNomination, and provide a theoretical analysis of its accuracy.Our algorithm possesses various desirable features. In particular, it does notrequire an explicit partitioning of the agents, as previous algorithms in theliterature. We show empirically that it achieves higher accuracy than theexiting algorithms over several metrics.

PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection