Decision-making often involves ranking and selection. For example, to assemble a team of political forecasters, we might begin by narrowing our choice set to the candidates we are confident rank among the top 10% in forecasting ability. Unfortunately, we do not know each candidate's true ability but observe a noisy estimate of it. This paper develops new Bayesian algorithms to rank and select candidates based on noisy estimates. Using simulations based on empirical data, we show that our algorithms often outperform frequentist ranking and selection algorithms. Our Bayesian ranking algorithms yield shorter rank confidence intervals while maintaining approximately correct coverage. Our Bayesian selection algorithms select more candidates while maintaining correct error rates. We apply our ranking and selection procedures to field experiments, economic mobility, forecasting, and similar problems. Finally, we implement our ranking and selection techniques in a user-friendly Python package documented here: https://dsbowen-conditional-inference.readthedocs.io/en/latest/.

中文翻译：

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贝叶斯排名和选择与实地研究、经济流动性和预测的应用

决策通常涉及排名和选择。例如，要组建一支政治预测团队，我们可能首先将选择范围缩小到我们有信心在预测能力方面排名前 10% 的候选人。不幸的是，我们不知道每个候选人的真实能力，而是观察到对其的嘈杂估计。本文开发了新的贝叶斯算法来根据噪声估计对候选者进行排名和选择。使用基于经验数据的模拟，我们表明我们的算法通常优于常客排名和选择算法。我们的贝叶斯排名算法产生更短的排名置信区间，同时保持大致正确的覆盖率。我们的贝叶斯选择算法在保持正确错误率的同时选择更多候选者。我们将排名和选择程序应用于现场实验，经济流动性、预测和类似问题。最后，我们在此处记录的用户友好的 Python 包中实现了我们的排名和选择技术：https://dsbowen-conditional-inference.readthedocs.io/en/latest/。