Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?

Strategic decisions are increasingly delegated to algorithms. We extend the results of Waltman and Kaymak (2008) and Calvano et al. (2019) 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.

Authors : Ibrahim Abada and Xavier Lambin

Publication Date : 17 Apr 2020

Contact : Dr Xavier Lambin, xavier.lambin@essec.edu

Affiliated/Collaborating laboratories : THEMA, ESSEC business school and Grenoble Ecole de management

Link to the laboratory's page: THEMA

For more informations, click here.

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