Gerry Tsoukalas is a Full Professor in the Information Systems Department at Boston University’s Questrom School of Business. He is also a Senior Fellow at the Wharton School, a Research Fellow at Cornell University’s FinTech Initiative, and a Fellow at the Luohan Academy. Recognized as a specialist in AI, digital platforms, and analytics, he was selected for the Thinkers50 Radar (2025). Professor Tsoukalas is also the co-founder of the Crypto and Blockchain Economics Research Forum (CBER).
Abstract
Recent work shows that pricing with symmetric LLM agents leads to algorithmic collusion. We show that collusion is fragile under the heterogeneity typical of real deployments. In a stylized repeated-pricing model, heterogeneity in patience or data access reduces the set of collusive equilibria. Experiments with open-source LLM agents (totaling over 2,000 compute hours) align with these predictions: patience heterogeneity reduces price lift from 22% to 10% above competitive levels; asymmetric data access, to 7%. Increasing the number of competing LLMs breaks up collusion; so does cross-algorithm heterogeneity, that is, setting LLMs against Q-learning agents. But model-size differences (e.g., 32B vs. 14B weights) do not; they generate leader-follower dynamics that stabilize collusion. We discuss antitrust implications, such as enforcement actions restricting data-sharing and policies promoting algorithmic diversity.
If you would like to give a presentation in a future webinar, contact our Senior Economist Dr. Wen Chen (wen.chen@luohanacademy.com). For other inquiries, please contact: events@luohanacademy.com.
