StreamingBandit: Experimenting with Bandit Policies
Dublin Core
Title
StreamingBandit: Experimenting with Bandit Policies
Subject
sequential decision-making, multi-armed bandit, data streams, sequential experimentation, Python.
Description
A large number of statistical decision problems in the social sciences and beyond can
be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard
to develop and evaluate policies that tackle these types of problems, and to use such
policies in applied studies. To address this issue, this paper introduces StreamingBandit, a Python web application for developing and testing bandit policies in field studies.
StreamingBandit can sequentially select treatments using (online) policies in real time.
Once StreamingBandit is implemented in an applied context, different policies can be
tested, altered, nested, and compared. StreamingBandit makes it easy to apply a multitude of bandit policies for sequential allocation in field experiments, and allows for the
quick development and re-use of novel policies. In this article, we detail the implementation logic of StreamingBandit and provide several examples of its use
be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard
to develop and evaluate policies that tackle these types of problems, and to use such
policies in applied studies. To address this issue, this paper introduces StreamingBandit, a Python web application for developing and testing bandit policies in field studies.
StreamingBandit can sequentially select treatments using (online) policies in real time.
Once StreamingBandit is implemented in an applied context, different policies can be
tested, altered, nested, and compared. StreamingBandit makes it easy to apply a multitude of bandit policies for sequential allocation in field experiments, and allows for the
quick development and re-use of novel policies. In this article, we detail the implementation logic of StreamingBandit and provide several examples of its use
Creator
Jules Kruijswijk
Source
https://www.jstatsoft.org/article/view/v094i09
Publisher
Tilburg University
Date
June 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Jules Kruijswijk, “StreamingBandit: Experimenting with Bandit Policies,” Repository Horizon University Indonesia, accessed June 28, 2025, https://repository.horizon.ac.id/items/show/8142.