StreamingBandit: Experimenting with Bandit Policies

Abstract

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.

Fields of Gold: Scraping Web Data for Marketing Insights

“…This means that researchers not only use APIs to retrieve data but can also operate their own APIs to examine real marketplaces (e.g., using rplumber.io in R). Researchers in data science, for example, offer firms a framework for testing multiarmed bandit policies via APIs while at the same time gathering field experimental data (Kruijswijk et al 2020). Marketing researchers could use similar API-powered microservices to study emerging topics such as recommendation systems (and resulting biases) or tap into a firm’s customer relationship management system to validate new customer churn models.…”