Strategic choice of price-setting algorithms

Katrin Buchali, Jens Grüb, Matthias Muijs and Ulrich Schwalbe

No 01-2023, Hohenheim Discussion Papers in Business, Economics and Social Sciences from University of Hohenheim, Faculty of Business, Economics and Social Sciences

Abstract: Recent experimental simulations have shown that autonomous pricing algorithms are able to learn collusive behavior and thus charge supra-competitive prices without being explicitly programmed to do so. These simulations assume, however, that both firms employ the identical price-setting algorithm based on Q-learning. Thus, the question arises whether the underlying assumption that both firms employ a Q-learning algorithm can be supported as an equilibrium in a game where firms can chose between different pricing rules. Our simulations show that when both firms use a learning algorithm, the outcome is not an equilibrium when alternative price setting rules are available. In fact, simpler price setting rules as for example meeting competition clauses yield higher payoffs compared to Q-learning algorithms.

Keywords: pricing algorithms; algorithmic collusion; reinforcement learning (search for similar items in EconPapers)
JEL-codes: D43 D83 L13 L49 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-bec, nep-com, nep-gth, nep-ind and nep-reg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hohdps:012023

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