A Heuristic Approach to Explore: The Value of Perfect Information

• 成果类型: Article
• 署名作者: Tehrani, Shervin Shahrokhi; Ching, Andrew T.
• 署名单位: University of Texas System; University of Texas Dallas; Johns Hopkins University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2019.00578
• 发表日期: 2024
• 关键词: exploration-exploitation tradeoff; heuristics; multiarmed bandits; experiential learning; bounded rationality; cognitive tractability

摘要

This research introduces a new heuristic decision model called myopic-value of perfect information (VPI) to study multiarmed bandit (MAB) problems. The myopic-VPI approach only involves ranking the alternatives and computing a one-dimensional integration to obtain the expected future value of exploration. Because myopic-VPI is intuitive and does not involve solving a dynamic programming problem, it has the potential to serve as a useful heuristic approach to model exploration-exploitation tradeoffs. We conduct a series of simulation experiments to study its performance relative to other heuristics under a wide range of parameterizations. We find that myopic-VPI provides significant savings in computational time and decent performance in accumulated utility (although not the strongest) relative to other forward-looking heuristics; this suggests that it is a useful fast-and-frugal heuristic. Furthermore, our simulation experiments also reveal the conditions under which myopic-VPI outperforms and underperforms compared with other heuristics. Its empirical performance in the diaper category further shows that myopic-VPI can save estimation time significantly and fit the data on par with index and near-optimal, providing encouraging news that myopic-VPI could be added to the researcher's or practitioner's toolkit for MAB problems.