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How language model agents reshape price competition

Martin Bichler, Philip Hierhager, Kassian Köck, Fabian Pieroth · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.21711 · cited by 0 works (OpenAlex) · open-access PDF

French

La concurrence répétée de Bertrand est un critère de référence strict pour les prix algorithmiques: sur les marchés homogènes de biens, les procédures d'apprentissage standard choisissent généralement le résultat du jeu Nash avec des prix proches du coût marginal. Nous montrons que les agents de tarification LLM peuvent échapper à ce critère, en fonction des décisions de déploiement. Les LLMs surpassent les apprenants en ligne standard par une adaptation plus rapide, mais cet avantage disparaît largement contre les apprenants en pleine information. Un algorithme de substitution extrait illustre comment les LLMs dans des déploiements spécifiques tirent profit de la sous-traitance régulière des apprenants plus lents. Les agents qui post

Machine translation (Meta M2M100); the English original follows.

Abstract

Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices near marginal cost. We show that LLM pricing agents can escape this benchmark, depending on deployment decisions. LLMs outperform standard online learners through faster adaptation, but this advantage largely disappears against full-information learners. An extracted surrogate algorithm illustrates how LLMs in specific deployments profit from regularly undercutting slower learners. Agents that post one price every period from raw market histories that include rival prices can sustain supracompetitive regimes in self-play. Agents that periodically emit probability distributions from summarized histories of their own profits instead move toward the competitive region in self-play. Markets mixing these interfaces can generate dynamic regimes in which LLMs establish a high-price anchor and regularly undercut it to capture demand. These regimes can keep both competitors' prices above marginal cost. Deployment choices shape these outcomes, shifting the focus from stationary equilibria to dynamic pricing regimes.

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Topics: Economics, Microeconomics, Outcome (game theory), Competition (biology), Profit (economics), Dynamic pricing

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