PeerNomination：放松精确性以提高对等点选择的准确性
PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection
摘要：
在同行选择代理必须选择自己的一个子集的奖励或奖品。由于代理人是自私的，我们希望设计公平的算法，这样单个代理人就不会影响自己被选中的机会。这个问题在资源分配和机制设计中有着广泛的应用，并在人工智能文献中得到了大量的关注。在这里，我们提出了一种新的算法，公平的对等点选择，PeerNomination，并提供其准确性的理论分析。我们的算法具有各种理想的功能。特别是，它不需要一个明确的分区的代理，在文献中以前的算法。我们的经验表明，它实现了更高的精度比现有的算法在几个指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
