Algorithmic Pricing and Algorithmic Collusion
Martin Bichler, Julius Durmann, Matthias Oberlechner
TL;DR
This paper investigates whether learning-enabled pricing agents can sustain tacit collusion and supra-competitive prices in repeated oligopoly settings, bridging online learning with game-theoretic concepts. It surveys Bertrand-type models, online bandit learning, and reinforcement learning to illustrate how algorithms may converge to collusive outcomes under certain dynamics, while noting the lack of a comprehensive theory. The work outlines concrete BISE research opportunities across algorithm design, detection, regulation, accountability, and applications beyond traditional oligopolies. It discusses regulatory gaps and emphasizes the practical importance of transparency and monitoring to mitigate potential anti-competitive risks in digital marketplaces. Overall, the article highlights the interdisciplinary challenges at the intersection of computer science, economics, and policy, and calls for targeted research to understand and manage algorithmic competition in online ecosystems.
Abstract
The rise of algorithmic pricing in online retail platforms has attracted significant interest in how autonomous software agents interact under competition. This article explores the potential emergence of algorithmic collusion - supra-competitive pricing outcomes that arise without explicit agreements - as a consequence of repeated interactions between learning agents. Most of the literature focuses on oligopoly pricing environments modeled as repeated Bertrand competitions, where firms use online learning algorithms to adapt prices over time. While experimental research has demonstrated that specific reinforcement learning algorithms can learn to maintain prices above competitive equilibrium levels in simulated environments, theoretical understanding of when and why such outcomes occur remains limited. This work highlights the interdisciplinary nature of this challenge, which connects computer science concepts of online learning with game-theoretical literature on equilibrium learning. We examine implications for the Business & Information Systems Engineering (BISE) community and identify specific research opportunities to address challenges of algorithmic competition in digital marketplaces.