The Use of Machine Learning Techniques and Distance Measures in Capturing Collusive Pricing: A Case Study for Algorithmic Pricing in E-Commerce Industry

Abstract

The rapid progress of “Machine Learning” (ML) and “Artificial Intelligence” (AI) is altering the dynamics of competition such as pricing and production, market regulation and common understanding of economies. This paper examines the complex link between algorithmic pricing strategies and contemporary market dynamics, offering insights into their multi-layered implications. Our findings indicate a systematic relationship between algorithmic pricing and competitive dynamics. Price dispersion grows with the possibility of being an algorithmic seller in online sale of milk products, indicating a non-collusive pricing strategy takes place. On the other hand, quality differences and higher production within a market may result in a collusive pricing scheme in online markets where pricing choices are made by machines. JEL Classification: C51, D43, L41