Improving the gold standard in field experiments with multi-armed bandits

Kaibel, Chris ; Biemann, Torsten

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Document Type: Conference or workshop publication
Year of publication: 2017
The title of a journal, publication series: Annual Meeting Proceedings / Academy of Management
Volume: 2017
Page range: 16350
Conference title: Academy of Management 2017 Annual Meeting
Location of the conference venue: Atlanta, GA
Date of the conference: 04.-08.08.2017
Place of publication: Chicago, IL
Publishing house: Academy of Management
ISSN: 0065-0668 , 2151-6561
Publication language: English
Institution: Business School > ABWL, Personalmanagement u. Führung (Biemann 2013-)
Subject: 330 Economics
Abstract: In field experiments, researchers commonly allocate subjects to different treatment conditions before the experiment starts. While this approach is intuitive, new information gathered during the experiment is not considered. Based on methodological approaches from other scientific fields such as computer science and medicine, we suggest a randomized adaptive allocation for field experiments in organizational research that is based on a Bayesian multi-armed bandit algorithm. By means of Monte Carlo simulations, we test the usefulness of this approach in a comparison with randomized controlled trials that have a fixed and balanced subject allocation. Our findings suggest that randomized adaptive allocation is more efficient in most settings. We develop recommendations for researchers and discuss limitations of our approach.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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