Robust Sequential Search

• Tác giả: Karl H Schlag, Andriy Zapechelnyuk
• Ngôn ngữ: eng
• Ký hiệu phân loại: 025.524 Information search and retrieval
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 164959

Tóm tắt

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.