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

Japanese

繰り返しのBertrand競争は、アルゴリズム価格のための厳格な基準である: 同一の商品市場では、標準的な学習手順は、通常、フェーズゲームのNashの結果に焦点を当て、価格の近くの限界コストに焦点を当てています。私たちは、LLM価格代理人は、展開決定に応じて、この基準を逃れることができます。LLMは、より迅速な適応を通じて標準的なオンライン学習者を上回りますが、この利点は、ほとんどが完全な情報学習者に対して消失します。抽出された代替アルゴリズムは、特定の展開のLLMは、定期的に遅い学習者を低下させることから利益を生み出す方法を示しています。 LLMの競合価格を含む原価ストーリーから

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange