Detecting Resale Price Maintenance with Unsupervised Machine Learning

Abstract Computational antitrust, the data-driven investigation of potential antitrust violations, has found more and more applications in recent years, including through the use of machine learning. However, the availability of labelled data to train algorithms proves to be an obstacle. In this paper, we explore the use of unsupervised machine learning to detect resale price maintenance (RPM) in price data. We develop assumptions that RPM prices exhibit increased similarity, a right-skewed distribution including a cut-off point, and fewer price changes over time compared with non-RPM prices. Based on these assumptions, we extract features based on simple statistical coefficients and perform clustering to detect products with price characteristics consistent with RPM. Subsequently, this can serve as a sufficient basis to conduct more in-depth antitrust investigations. We test our approach on five real-world product datasets scraped from a price comparison website. We show that our screen successfully clusters products with price patterns indicative of RPM.