StreamingBandit : Experimenting with Bandit Policies

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Pysyväisosoite

http://hdl.handle.net/10138/324462

Lähdeviite

Kruijswijk , J , van Emden , R , Parvinen , P & Kaptein , M 2020 , ' StreamingBandit : Experimenting with Bandit Policies ' , Journal of Statistical Software , vol. 94 , no. 9 , pp. 1-47 . https://doi.org/10.18637/jss.v094.i09

Julkaisun nimi: StreamingBandit : Experimenting with Bandit Policies
Tekijä: Kruijswijk, Jules; van Emden, Robin; Parvinen, Petri; Kaptein, Maurits
Tekijän organisaatio: Department of Economics and Management
Department of Forest Sciences
Forest Economics, Business and Society
Päiväys: 2020-08
Kieli: eng
Sivumäärä: 47
Kuuluu julkaisusarjaan: Journal of Statistical Software
ISSN: 1548-7660
DOI-tunniste: https://doi.org/10.18637/jss.v094.i09
URI: http://hdl.handle.net/10138/324462
Tiivistelmä: 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.
Avainsanat: sequential decision-making
multi-armed bandit
data streams
sequential experimentation
Python
CLINICAL-TRIALS
112 Statistics and probability
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: cc_by
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: publishedVersion


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