Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?

• 成果类型: Article
• 署名作者: Miklos-Thal, Jeanine; Tucker, Catherine
• 署名单位: University of Rochester; Massachusetts Institute of Technology (MIT)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2019.3287
• 发表日期: 2019
• 页码: 1552-1561
• 关键词: algorithms; collusion; demand prediction; forecasting; Machine Learning; economics: microeconomic behavior

摘要

We build a game-theoretic model to examine how better demand forecasting resulting from algorithms, machine learning, and artificial intelligence affects the sustainability of collusion in an industry. We find that, although better forecasting allows colluding firms to better tailor prices to demand conditions, it also increases each firm's temptation to deviate to a lower price in time periods of high predicted demand. Overall, our research suggests that, despite concerns expressed by policy makers, better forecasting and algorithms can lead to lower prices and higher consumer surplus.