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Multi‐armed bandit experiments in the online service economy

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  • Steven L. Scott

Abstract

The modern service economy is substantively different from the agricultural and manufacturing economies that preceded it. In particular, the cost of experimenting is dominated by opportunity cost rather than the cost of obtaining experimental units. The different economics require a new class of experiments, in which stochastic models play an important role. This article briefly summarizes multi‐armed bandit experiments, where the experimental design is modified as the experiment progresses to reduce the cost of experimenting. Special attention is paid to Thompson sampling, which is a simple and effective way to run a multi‐armed bandit experiment. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Steven L. Scott, 2015. "Multi‐armed bandit experiments in the online service economy," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 37-45, January.
    • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:1:p:37-45
    DOI: 10.1002/asmb.2104
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    Cited by:

    1. Hamsa Bastani & David Simchi-Levi & Ruihao Zhu, 2022. "Meta Dynamic Pricing: Transfer Learning Across Experiments," Management Science, INFORMS, vol. 68(3), pages 1865-1881, March.
    2. Ramesh Johari & Pete Koomen & Leonid Pekelis & David Walsh, 2022. "Always Valid Inference: Continuous Monitoring of A/B Tests," Operations Research, INFORMS, vol. 70(3), pages 1806-1821, May.
    3. Caio Waisman & Harikesh S. Nair & Carlos Carrion, 2025. "Online Causal Inference for Advertising in Real-Time Bidding Auctions," Marketing Science, INFORMS, vol. 44(1), pages 176-195, January.
    4. David Simchi-Levi & Chonghuan Wang, 2026. "Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk," Management Science, INFORMS, vol. 72(2), pages 1157-1174, February.
    5. Xu, Jianyu & Chen, Lujie & Tang, Ou, 2021. "An online algorithm for the risk-aware restless bandit," European Journal of Operational Research, Elsevier, vol. 290(2), pages 622-639.
    6. David Simchi-Levi & Chonghuan Wang, 2025. "Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference," Management Science, INFORMS, vol. 71(6), pages 4828-4846, June.

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