Algorithmic Pricing, Collusion & Market Dynamics: Navigating the New Frontier of Digital Competition

The integration of algorithmic pricing strategies in various markets raises pivotal questions about their effects on pricing dynamics, competitive behavior, and consumer welfare. This final project, an extended literature review, utilizes a multidisciplinary approach to examine these strategies’ impact, focusing on online retail and gasoline markets. It explores how algorithms condition prices based on competitors’ actions, influencing market competition and pricing structures. Theoretical models indicate that widespread adoption of algorithmic pricing can lead to increased profit margins and higher market prices, especially where these strategies are prevalent. This suggests that firms using pricing algorithms might inadvertently align their pricing strategies, mimicking a collusive equilibrium, which avoids price wars and establishes higher industry price levels. This unintended coordination occurs as algorithms, designed to maximize profits, react to competitors’ pricing moves to preserve or enhance profit margins rather than pursue aggressive price undercutting. Empirical evidence supports these models, particularly highlighting pronounced effects in oligopolistic markets—markets characterized by few but highly competitive firms. In such settings, algorithmic pricing by a few firms can stabilize higher price levels, reduce price competition, and potentially limit consumer choices and market entry for new competitors. By merging insights from computer science and economics, this study contributes to the debate on algorithmic pricing, offering a nuanced view of how such mechanisms affect market competition and policy formulation. The findings suggest that while algorithmic pricing can enhance pricing efficiency and responsiveness, it also poses significant risks of fostering non-competitive behaviors among market participants. This review highlights the importance of considering market structure and competitive dynamics when assessing the implications of algorithmic pricing.