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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

Arabic

تظهر المنافسة Bertrand المتكررة نموذجًا صارمًا لتقييم الأسعار الفوتوغرافي: في أسواق السلع المشتركة، فإن إجراءات التعلم القياسية عادة ما تختار النتائج في مرحلة Nash مع الأسعار بالقرب من تكلفة الحد الأدنى. ونحن نتعرف على أن مكاتب أسعار LLM يمكن أن تخفف هذه النتائج، اعتمادًا على قرارات التنفيذ. LLMs تتجاوز المتعلمين على الإنترنت القياسيين من خلال التكيف السريع، ولكن هذه الفائدة تنتقل إلى حد كبير ضد المتعلمين ذوي المعلومات الكاملة. يظهر تقويم استبدال LLMs في تطبيقات محددة فائدة من تراجع المتعلمين السريع بشكل منتظم. المكاتب التي تنشر أسعارًا

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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