Collusive Pricing Under LLM

TL;DR

This paper analyzes how large language models can facilitate collusive pricing in duopolies, revealing a phase transition influenced by model fidelity and retraining frequency that determines whether prices are competitive or collusive.

Contribution

It introduces a model linking LLM parameters to collusion dynamics, demonstrating how robustness and retraining affect market outcomes in a novel way.

Findings

A critical output-fidelity threshold induces a phase transition between competition and collusion.

Perfect fidelity leads to full collusion regardless of initial conditions.

Infrequent retraining with larger batch sizes amplifies collusive behavior.

Abstract

We study how delegating pricing to large language models (LLMs) can facilitate collusion in a duopoly when both sellers rely on the same pre-trained model. The LLM is characterized by (i) a propensity parameter capturing its internal bias toward high-price recommendations and (ii) an output-fidelity parameter measuring how tightly outputs track that bias; the propensity evolves through retraining. We show that configuring LLMs for robustness and reproducibility can induce collusion via a phase transition: there exists a critical output-fidelity threshold that pins down long-run behavior. Below it, competitive pricing is the unique long-run outcome. Above it, the system is bistable, with competitive and collusive pricing both locally stable and the realized outcome determined by the model's initial preference. The collusive regime resembles tacit collusion: prices are elevated on average, yet occasional low-price recommendations provide plausible deniability. With perfect fidelity, full collusion emerges from any interior initial condition. For finite training batches of size $b$, infrequent retraining (driven by computational costs) further amplifies collusion: conditional on starting in the collusive basin, the probability of collusion approaches one as $b$ grows, since larger batches dampen stochastic fluctuations that might otherwise tip the system toward competition. The indeterminacy region shrinks at rate $O(1/\sqrt{b})$.