Algorithmic Pricing, Market Outcomes, and Antitrust Concerns: Lessons from Recent Literature
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Hassan Faghani explores the extensive use of algorithmic pricing and its implications for the economy, firm strategies, and regulatory challenges. Advances in technology and data availability have enabled algorithmic pricing to be more accessible and widely used, especially with the rapid development of artificial intelligence (AI).
Dr. Faghani examines how algorithmic pricing differs from traditional methods, especially in markets with few players, and explores the role of learning mechanisms in these algorithms. He contrasts asynchronous learning, based on actual actions, with synchronous learning, involving counterfactual analysis, and discusses how factors like algorithm design and data availability influence these approaches.
Dr. Faghani argues that algorithmic pricing should be seen as a market outcome focused on profit maximization, similar to traditional industrial organization models, , and antitrust concerns should be analyzed accordingly.
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