  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
problem, 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.
