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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

German

Wiederholter Bertrand-Wettbewerb ist ein strenger Benchmark für algorithmische Preisgestaltung: In homogenen Warenmärkten wählen Standard-Lernverfahren typischerweise das Stadium-Spiel-Nash-Ergebnis mit Preisen in der Nähe von marginaler Kosten aus. Wir zeigen, dass LLM-Preisagenturen diesem Benchmark entgehen können, abhängig von den Bereitstellungsentscheidungen. LLMs übertreffen Standard-Online-Lernende durch schnellere Anpassung, aber dieser Vorteil verschwindet weitgehend gegenüber Vollinformations-Lernenden. Ein extrahiertes Surrogate-Algorithmus zeigt, wie LLMs in spezifischen Bereitstellungen Gewinne erzielen, wenn sie regelmäßig langsame Lernende untergraben. Agenten, die jeden Zeitraum

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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