Newest papers

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

Chinese

重复的伯特兰竞争是算法定价的严格基准:在均匀的商品市场,标准学习程序通常选择了阶段的Nash结果与价格接近边际成本的焦点。我们表明LLM定价代理可以逃脱这个基准,取决于部署的决定。LLM通过更快的调整超过标准的在线学习者,但这种优势在很大程度上消失在完全信息的学习者面前。一个提取的替代算法说明了特定部署的LLM如何从经常削弱较慢的学习者中获利。从原始市场历史中发布一个价格的代理人,其中包括竞争对手的价格,可以维持超竞争性的自律模式。定期发出从其自身利润的历史总结中提取的概率分布的代理人,而不是向自律竞争的区域移动。市场混合这些界面

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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