2020/04/30 by Nicholas Mattei, Paolo Turrini, Mattei, Nicholas +3 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #91A80 #91B10 #91B12 #91B14 #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Applications #Game Theory and Voting Systems #I.2 #J.4 #Multiagent Systems (cs.MA)
paper · pdf · doi:10.48550/arxiv.2004.14939
openalex publication_date 2020/04/30 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In peer selection agents must choose a subset of themselves for an award or a\nprize. As agents are self-interested, we want to design algorithms that are\nimpartial, so that an individual agent cannot affect their own chance of being\nselected. This problem has broad application in resource allocation and\nmechanism design and has received substantial attention in the artificial\nintelligence literature. Here, we present a novel algorithm for impartial peer\nselection, PeerNomination, and provide a theoretical analysis of its accuracy.\nOur algorithm possesses various desirable features. In particular, it does not\nrequire an explicit partitioning of the agents, as previous algorithms in the\nliterature. We show empirically that it achieves higher accuracy than the\nexiting algorithms over several metrics.\n