论文标题
强大的顺序搜索
Robust Sequential Search
论文作者
论文摘要
我们研究没有先验的顺序搜索。我们的兴趣在于决策规则，这些规则几乎是在每个历史记录之前和之后的最佳选择。我们将这些规则动态强大地称为。搜索文献采用了基于不动态强大的截止策略的最佳规则。我们得出动态强大的规则，并表明它们的性能超过了针对二进制环境的最佳效果的1/2，而对所有环境的最佳效果则超过了最佳的1/4。例如，如果外部选项超过最高替代方案的1/6，则该性能会大大改善最佳的2/3。
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.
