论文标题
同学：放松精确性以提高同伴选择的精度
PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection
论文作者
论文摘要
在同伴中，选择代理必须选择自己的子集进行奖励或奖品。由于代理人是自私的，我们想设计公正的算法，以便单个代理人不能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具有广泛的应用，并在人工智能文献中受到了极大的关注。在这里，我们提出了一种新颖的算法，用于选择公正的同伴选择，peernomination，并提供了对其准确性的理论分析。我们的算法具有各种理想的特征。特别是，它不需要像文献中的先前算法那样对代理进行明确的分区。我们从经验上表明，它的准确性比几个指标的退出算法更高。
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.
