Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?

• 成果类型: Article
• 署名作者: Abada, Ibrahim; Lambin, Xavier
• 署名单位: Grenoble Ecole Management; Engie; ESSEC Business School
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4623
• 发表日期: 2023
• 页码: 5042-5065
• 关键词: Machine learning; multiagent reinforcement learning; algorithmic decision making; tacit collusion; decentralized power systems

摘要

Strategic decisions are increasingly delegated to algorithms. We extend previous results of the algorithmic collusion literature to the context of dynamic optimization with imperfect monitoring by analyzing a setting where a limited number of agents use simple and independent machine-learning algorithms to buy and sell a storable good. No specific instruction is given to them, only that their objective is to maximize profits based solely on past market prices and payoffs. With an original application to battery operations, we observe that the algorithms learn quickly to reach seemingly collusive decisions, despite the absence of any formal communication between them. Building on the findings of the existing literature on algorithmic collusion, we show that seeming collusion could originate in imperfect exploration rather than excessive algorithmic sophistication. We then show that a regulator may succeed in disciplining the market to produce socially desirable outcomes by enforcing decentralized learning or with adequate intervention during the learning process.