Rethinking the gold standard with multi-armed bandits: Machine learning allocation algorithms for experiments

DOI:	https://doi.org/10.1177/1094428119854153
URL:	https://journals.sagepub.com/doi/abs/10.1177/10944...
Weitere URL:	https://www.researchgate.net/publication/333725629...
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2021
Titel einer Zeitschrift oder einer Reihe:	Organizational Research Methods : ORM
Band/Volume:	24
Heft/Issue:	1
Seitenbereich:	78-103
Ort der Veröffentlichung:	Thousand Oaks, CA
Verlag:	Sage
ISSN:	1094-4281 , 1552-7425
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Betriebswirtschaftslehre > ABWL, Personalmanagement u. Führung (Biemann 2013-)
Fachgebiet:	330 Wirtschaft
Freie Schlagwörter (Englisch):	experiments, randomized controlled trial, multi-armed bandit, exploration versus exploitation, machine learning, ethics in research
Abstract:	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.