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A Heuristic Approach to Explore: The Value of Perfect Information

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  • Shervin Shahrokhi Tehrani

    (Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080)

  • Andrew T. Ching

    (Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202)

Abstract

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.

Suggested Citation

  • Shervin Shahrokhi Tehrani & Andrew T. Ching, 2024. "A Heuristic Approach to Explore: The Value of Perfect Information," Management Science, INFORMS, vol. 70(5), pages 3200-3224, May.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:5:p:3200-3224
    DOI: 10.1287/mnsc.2019.00578
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