Artificial Intelligence, Algorithmic Pricing, and Collusion

• 成果类型: Article
• 署名作者: Calvano, Emilio; Calzolari, Giacomo; Denicolo, Vincenzo; Pastorello, Sergio
• 署名单位: University of Bologna; University of Bologna; University of Bologna; European University Institute; Center for Economic & Policy Research (CEPR); University of Bologna
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer20190623
• 发表日期: 2020
• 页码: 3267-3297
• 关键词: REINFORCEMENT; oligopoly; games; COMPETITION; models; agents; go

摘要

Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with. one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty.