Robust sequential search
研究了在没有先验信息的情况下，如何设计稳健的序贯搜索决策规则，使其在每个先验和每个历史下都接近最优，并给出了在独立同分布环境下的性能保证。
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 robust. The search literature employs optimal rules based on cutoff strategies, and these rules are not robust. We derive robust rules and show that their performance exceeds 1/2 of the optimum against binary independent and identically distributed (i.i.d.) environments and 1/4 of the optimum against all i.i.d. 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.