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multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

Management Science 2024年第6期71卷 iv-vi, 4533-5418页

作者： David Simchi-Levi Chonghuan Wang Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem . We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://***/10.1287/mnsc.2023.00492

关键词： adaptive experimental design multi-armed bandit minimax multi-objective optimization Pareto optimality

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