Algorithmic price recommendations and collusion: Experimental evidence - Matthias Hunold; Tobias Werner (Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), 2023)
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: C92; D43; L13; L41; 330; Collusion; Experiment; Human-Machine Interaction; Bertrand Oligopoly