Algorithmic Price Recommendations and Collusion: Experimental Evidence

67 Pages Posted: 20 Sep 2023

See all articles by Matthias Hunold

Matthias Hunold

University Siegen

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: August 30, 2023

Abstract

This paper investigates the collusive and competitive effects of algorithmic price recommendations on market outcomes. These recommendations are often non-binding and common in many markets, especially on online platforms. We develop a theoretical framework and derive two algorithms that recommend collusive pricing strategies. Utilizing a laboratory experiment, we find that sellers condition their prices on the recommendation of the algorithms. The algorithm with a soft punishment strategy lowers market prices and has a pro-competitive effect. The algorithm that recommends a subgame perfect equilibrium strategy increases the range of market outcomes, including more collusive ones. Variations in economic preferences lead to heterogeneous treatment effects and explain the results.

Keywords: Collusion, Experiment, Human–Machine Interaction, Bertrand Oligopoly

JEL Classification: C92, D43, L13, L41

Suggested Citation

Hunold, Matthias and Werner, Tobias, Algorithmic Price Recommendations and Collusion: Experimental Evidence (August 30, 2023). Available at SSRN: https://ssrn.com/abstract=4557050 or http://dx.doi.org/10.2139/ssrn.4557050

Matthias Hunold

University Siegen ( email )

Unteres Schloß 3
Siegen
Germany

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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