Evidence‐based Management and Leadership

Briner, Rob B.; Walshe, Neil D.

doi:10.1002/9781118326404.ch3

Cited by 2 publications

(1 citation statement)

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“…External evidence may be scientific evidence in the form of metaanalyses or field experiments conducted in other organizations. Therefore, the use of Bayesian MABs is particularly interesting in the context of evidence-based management (Bouffard & Reid, 2012;Briner, Denyer, & Rousseau, 2009;Briner & Walshe, 2013;Reay, Berta, & Kohn, 2009) and organizations' interest in workforce analytics practices.…”

Section: Future Applications and Extension For Mab Approachesmentioning

confidence: 99%

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

Kaibel

Biemann

2019

Organizational Research Methods

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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.

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Section: Future Applications and Extension For Mab Approachesmentioning

confidence: 99%