摘要翻译：
我们研究无先验的顺序搜索。我们的兴趣在于在每一个之前和之后的历史中接近最优的决策规则。我们称这些规则为动态鲁棒的。搜索文献采用基于不动态鲁棒性的截止策略的最优规则。我们导出了动态鲁棒规则，并表明它们的性能在二进制环境下超过最优性能的1/2，在所有环境下超过最优性能的1/4。这种性能随着外部选项值的增加而大大提高，例如，如果外部选项超过最高可能选项的1/6，它就超过最佳选项的2/3。
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英文标题：
《Robust Sequential Search》
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作者：
Karl H. Schlag and Andriy Zapechelnyuk
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  We study sequential search without priors. Our interest lies in decision rules that are close to being optimal under each prior and after each history. We call these rules dynamically robust. The search literature employs optimal rules based on cutoff strategies that are not dynamically robust. We derive dynamically robust rules and show that their performance exceeds 1/2 of the optimum against binary environments and 1/4 of the optimum against all environments. This performance improves substantially with the outside option value, for instance, it exceeds 2/3 of the optimum if the outside option exceeds 1/6 of the highest possible alternative.
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PDF链接：
https://arxiv.org/pdf/2008.00502