Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246229 
Authors: 
Year of Publication: 
2021
Series/Report no.: 
DICE Discussion Paper No. 372
Publisher: 
Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf
Abstract: 
As self-learning pricing algorithms become popular, there are growing concerns among academics and regulators that algorithms could learn to collude tacitly on non-competitive prices and thereby harm competition. I study popular reinforcement learning algorithms and show that they develop collusive behavior in a simulated market environment. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with human participants in the same environment. Across different treatments, I vary the market size and the number of firms that use a self-learned pricing algorithm. I provide evidence that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, market 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.
Subjects: 
Artificial Intelligence
Collusion
Experiment
Human-Machine Interaction
JEL: 
C90
D83
L13
L41
ISBN: 
978-3-86304-371-1
Document Type: 
Working Paper

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