Rethinking the Gold Standard With Multi-armed Bandits: Machine Learning Allocation Algorithms for Experiments

• Published in: Organizational research methods Vol. 24; no. 1; pp. 78 - 103
• Main Authors: Kaibel, Chris, Biemann, Torsten
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2021; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Bayesian analysis; Clinical trials; Experiments; Machine learning; Multi-armed bandit problems; Research ethics; multi-armed bandit; ethics in research; machine learning; exploration versus exploitation; experiments; randomized controlled trial
• ISSN: 1094-4281; 1552-7425
• DOI: 10.1177/1094428119854153

In experiments, researchers commonly allocate subjects randomly and equally to the different treatment conditions before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest machine learning algorithms for subject allocation in experiments. Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo simulations to compare its efficiency with randomized controlled trials that have a fixed and balanced subject allocation. Our findings indicate that a randomized allocation based on Bayesian multi-armed bandits is more efficient and ethical in most settings. We develop recommendations for researchers and discuss the limitations of our approach.