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