Algorithmic Pricing and Algorithmic Collusion

2025-04-23

AI Summary

This paper addresses algorithmic collusion—the emergence of supra-competitive pricing among autonomous learning agents in online retail without explicit coordination. We integrate online learning theory with equilibrium learning frameworks in game theory, modeling the problem as a repeated Bertrand game and conducting simulations using Q-learning and related reinforcement learning algorithms, complemented by convergence analysis and equilibrium stability verification. Results demonstrate that specific reinforcement learning mechanisms can sustain prices persistently above the Nash equilibrium in simulated markets, thereby identifying the conditions and intrinsic mechanisms underlying algorithmic collusion. This work bridges a critical gap in the interpretability of algorithmic collusion theory, providing testable theoretical foundations and detection pathways for digital market regulation. It offers significant implications for Business & Information Systems Engineering (BISE) and interdisciplinary research on algorithmic governance.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Investigates algorithmic collusion in online retail pricing
• Explores supra-competitive outcomes from learning agent interactions
• Bridges online learning and game theory in digital markets

Innovation

• Algorithmic collusion from repeated learning agent interactions
• Reinforcement learning maintains supra-competitive pricing levels
• Interdisciplinary online learning and game theory equilibrium analysis