A Two-armed Bandit Framework for A/B Testing

This paper aims to develop effective A/B testing solutions across various industries, including internet companies such as Google, LinkedIn, X, and Meta, e-commerce platforms like Amazon, and two-sided marketplaces such as Airbnb. A/B testing has become the gold standard in these companies for policy evaluation and product deployment. For example, on traditional portal websites, it is common to assess a new version of a webpage (B) against the existing one (A) by randomly assigning visitors to either variant and then comparing an outcome of interest - such as the click through rate - to determine whether B outperforms A. A motivating application considered in this paper is the development of A/B testing solutions for large-scale fleet management in ride-sharing platforms, such as Uber and Lyft in the United States, and Didi Chuxing in China. The widespread adoption of smartphones and ride-sharing apps has enabled these companies to revolutionize and reshape urban transportation (Alonso-Mora et al., 2017; Hagiu and Wright, 2019). Ride-sharing platform is a typical two-sided market that enables efficient interactions between passengers and drivers (Rysman, 2009), as well as a complex spatio-temporal ecosystem (Wang and Y ang, 2019). Specifically, the demand and supply of this two-sided market can be measured by the numbers of call orders and the total drivers' online time in a city. These variables exhibit strong temporal patterns (see Figure 1 for a visualization), and interact with each other over time and location.