Algorithmic and Human Collusion

• 成果类型: Article; Early Access
• 署名作者: Werner, Tobias
• 署名单位: Maynooth University; University of Southampton; Heinrich Heine University Dusseldorf
• 刊物名称: ECONOMIC JOURNAL
• ISSN/ISSBN: 0013-0133
• DOI: 10.1093/ej/ueag013
• 发表日期: 2026
• 关键词: infinitely repeated games; tacit collusion; artificial-intelligence; Cournot oligopoly; COOPERATION; COMPETITION; go; COMMUNICATION; equilibrium; complexity

摘要

I study self-learning pricing algorithms and show that they are collusive in market simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with humans in the same environment. Across different treatments, I vary the market size and the number of firms that use a pricing algorithm. I demonstrate that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, prices are weakly increasing in the number of algorithms in the market. In three-firm markets, algorithms weaken competition if most firms use an algorithm and human sellers are inexperienced.