We consider a robust version of the full information best choice problem: there is model uncertainty, represented by a set of priors, about the measure driving the observed process. We propose a general construction of the set of priors that we use to solve the problem in the setting of Riedel (Econometrica 77(3):857–908, 2009). As in the classical case, it is optimal to stop if the current observation is a running maximum that exceeds certain decreasing thresholds. We characterize the history dependent minimizing measure and perform sensitivity analysis on two examples.

中文翻译：

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健壮的最佳选择问题

我们考虑完整信息最佳选择问题的一个可靠版本：存在由模型驱动的过程来衡量的模型不确定性，用一组先验表示。我们提出了一套先验集的一般构造，用于解决Riedel的问题（Econometrica 77（3）：857-908，2009）。与经典情况一样，如果当前观测值是超过某些减小阈值的运行最大值，则最好停止。我们描述了依赖历史的最小化度量，并对两个示例进行了敏感性分析。