Strategic choice of price-setting algorithms

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 01-2023

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection ; Search; Learning; Information and Knowledge; Communication; Belief; Unawareness ; Oligopoly and Other Imperfect Markets ; Antitrust Policy: Other
• Thema: pricing algorithms ; algorithmic collusion ; reinforcement learning

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Buchali, Katrin ; Grüb, Jens ; Muijs, Matthias ; Schwalbe, Ulrich
• Ereignis: Veröffentlichung
• (wer): Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
• (wo): Stuttgart
• (wann): 2023

Weitere Informationen

• Handle: http://hdl.handle.net/10419/268721
• URN: urn:nbn:de:bsz:100-opus-21276
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ