arXiv:2605.16064v2 Announce Type: replace-cross Abstract: We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets. We consider firms that price using a myopic estimate-then-optimize rule: each repeatedly fits a demand model to its own price and sales history and sets the price that maximizes estimated profit. This demand model is misspecified, omitting competitors' prices. We analyze the dynamics of this rule when it is initialized by an exploration phase of independent random prices. We characterize when this pipeline converge
This research provides timely evidence regarding the emergent behavior of simple AI pricing systems, aligning with the increasing deployment of autonomous agents in commercial settings.
The study highlights how even basic AI pricing algorithms, under specific conditions, can converge on supra-competitive prices, impacting market fairness and regulatory oversight.
The understanding of AI-driven market dynamics now includes a clear mechanism for implicit collusion without explicit coordination, necessitating new approaches to competition policy.
- · Firms deploying estimate-then-optimize AI pricing
- · AI agents developers focused on economic models
- · Regulatory technology platforms
- · Consumers
- · Antitrust regulators using traditional frameworks
- · Smaller firms unable to implement sophisticated AI pricing
Algorithmic pricing systems in competitive markets will lead to higher-than-expected prices over time.
This could trigger increased regulatory scrutiny and new antitrust legislation specifically targeting AI pricing strategies.
The development of 'anti-collusion' AI agents or regulatory 'black boxes' to monitor market pricing behaviors may become a new area of AI research and deployment.
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Read at arXiv cs.AI