How language model agents reshape price competition
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 supracompeti...
| Authors | Martin Bichler, Philip Hierhager, Kassian Köck et al. |
|---|---|
| Primary category | cs.GT |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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