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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

Korean

반복적인 Bertrand 경쟁은 알고리즘 가격 조정에 대한 엄격한 기준입니다 : 동일한 상품 시장에서 표준 학습 절차는 일반적으로 무상 비용에 가까운 가격에 초점을 맞춘 Nash 게임 결과를 선택합니다. 우리는 LLM 가격 에이전트가 배포 결정에 따라이 기준을 피할 수 있음을 보여줍니다. LLMs는 더 빠른 적응을 통해 표준 온라인 학습자를 뛰어넘지만이 이점은 대부분 전체 정보 학습자를 상대로 사라집니다. 추출 된 대체 알고리즘은 특정 배포에있는 LLM이 정기적으로 느린 학습자를 억제하는 것으로부터 이익을 얻는 방법을 보여줍니다. LLMs는 경쟁력있는 가격을 포함하는 원시 시장 역사에서 한 개의 가격을 게시하는 에이전트는 자율적으로 확률

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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