Language model pricing agents change how companies compete on price
How language model agents reshape price competition
Computer Science and Game Theory
Summary
Setting prices in markets where similar products compete is tough because typical computer programs tend to push prices down toward the lowest possible level. The authors found that pricing agents powered by large language models (LLMs) can behave differently and sometimes keep prices higher than usual depending on how they are set up. These language models adapt faster and can create more dynamic pricing strategies by sometimes undercutting competitors, leading to prices above cost. How the LLMs are deployed — what market information they use and how they decide prices — strongly influences whether they compete aggressively or sustain higher prices over time.
What this means in practice
- •For ecommerce pricing teams: Use LLM agents to create dynamic pricing strategies that can maintain prices above cost in competitive online markets.$Commercial implications: Enables development of smarter pricing software that adapts faster and strategically competes, improving profit margins for online sellers.
- •For dynamic pricing engineers: Design market simulations with varied LLM agent deployments to study and optimize pricing behaviors in competitive settings.
Authors
Martin Bichler, Philip Hierhager, Kassian Köck, Fabian Pieroth
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.