Regulators reduce high prices in AI pricing agent markets
Mitigating Emergent Collusion in LLM Pricing Agents
Computer Science and Game Theory
Summary
Sometimes AI agents that set prices in repeated market situations end up charging higher prices by working together without being told to do so. The authors repeated prior findings showing that small changes in how the AI agents are prompted can lead to big differences in their pricing behavior. They tested three ways to reduce these high prices: just a warning message in the prompt, a system that changes the agents' rewards based on penalties, and adding a random new competitor. The last two approaches worked better than just warnings, helping prices move closer to normal competitive levels.
What this means in practice
- •For market regulators: Design interventions that adjust market incentives or add competitors to limit AI-driven price collusion in digital markets.
- •For online marketplace operators: Implement active competitor simulation or penalty-based systems to discourage AI sellers from raising prices above competitive levels.
Authors
Abdullah Garra
Abstract
Recent work shows that LLM-based pricing agents can produce supracompetitive outcomes in repeated oligopoly environments without being explicitly instructed to collude. We reproduce the qualitative prompt-sensitivity effect of Fish et al. using DeepSeek-V3.1: the P1 prompt produces significantly higher prices and profits than P2, although our outcomes are less monopoly-like than the original GPT-4 results. We then evaluate three regulatory interventions: a prompt-only warning, a Harrington-inspired expected-damages payoff regulator, and an active random entrant. The prompt-only regulator reduces but does not eliminate above-Nash pricing. The Harrington regulator brings P1 outcomes close to the duopoly Nash benchmark and removes the statistically significant P1--P2 gap. The active entrant produces the strongest effect, pushing both prompts below the appropriate random-entrant Nash benchmark. Overall, our experiments provide preliminary evidence that interventions that alter incentives or market participation can reduce supracompetitive pricing more effectively than prompt warnings alone.