Algorithmic and Human Collusion

61 Pages Posted: 24 Nov 2021 Last revised: 15 Nov 2023

See all articles by Tobias Werner

Tobias Werner

Center for Humans and Machines / Max Planck Institute for Human Development; Heinrich Heine University Dusseldorf - Duesseldorf Institute for Competition Economics (DICE)

Date Written: November 14, 2023

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

Suggested Citation

Werner, Tobias, Algorithmic and Human Collusion (November 14, 2023). Available at SSRN: https://ssrn.com/abstract=3960738 or http://dx.doi.org/10.2139/ssrn.3960738

Tobias Werner (Contact Author)

Center for Humans and Machines / Max Planck Institute for Human Development ( email )

Berlin
Germany

Heinrich Heine University Dusseldorf - Duesseldorf Institute for Competition Economics (DICE) ( email )

Universitaetsstr. 1
Duesseldorf, NRW 40225
Germany

HOME PAGE: http://tfwerner.com

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