Algorithmic and Human Collusion
88 Pages Posted: 24 Nov 2021 Last revised: 6 Nov 2024
Date Written: November 06, 2024
Abstract
I study self-learning pricing algorithms and show that they are collusive in market simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with humans in the same environment. Across different treatments, I vary the market size and the number of firms that use a pricing algorithm. I demonstrate that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, 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.
Keywords: Artificial Intelligence, Collusion, Experiment, Human–Machine Interaction
JEL Classification: C90, D83, L13, L41
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