PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection
This addresses impartial selection in resource allocation and mechanism design, offering a novel approach that improves accuracy for applications like awards or prizes, though it appears incremental as it builds on prior work without a paradigm shift.
The paper tackles the problem of impartial peer selection where self-interested agents choose a subset of themselves, introducing the PeerNomination algorithm that relaxes exactness requirements. It shows empirically that PeerNomination achieves higher accuracy than existing algorithms across several metrics.
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.