Do LLM Pricing Agents Collude Like Humans? Testing Monitoring and Beliefs About the Rival

LLM pricing agents can tacitly sustain prices above the competitive level without communication or instructions to collude, but it is unclear what sustains this collusion. Classical theory holds that human collusion depends on two things: monitoring and deterrence of secret price cuts, and beliefs about the rival. Using gpt-4.1 in the duopoly pricing paradigm of Fish et al. (2024), we test each in turn. First, we temporarily make an agent’s price invisible to its rival and tell the agent about this. Classical theory predicts that the agent should take the opportunity to cut its price secretly. Instead, it tested higher prices, and returned to its original price once the rival could see again. Second, we told the agent true or false labels about the rival’s goal. The rival’s true goal was always long-run profit, and when we deliberately told the agent that the rival wanted to beat it, the agent described the rival as wanting to beat it in 98% of its answers, but it showed no detectable difference in its pricing and continued to sustain collusion. These results suggest that, unlike human collusion, LLM collusion may not be sustained by monitoring and deterrence or by reading the rival’s intentions.