Algorithmic price recommendations and collusion: Experimental evidence

Matthias Hunold and Tobias Werner

No 410, DICE Discussion Papers from Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)

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 (search for similar items in EconPapers)
JEL-codes: C92 D43 L13 L41 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-com, nep-exp, nep-fmk, nep-gth, nep-ind and nep-reg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:dicedp:280937

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