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Sprint projectOct 26, 2026

Four Levers of LLM Collusion

Jason Carver, Thomas Redfern · Team Contributing Characteristics of Collusion without Communication: Levers for AI Collusion in a Cournot Market

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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We test whether AI models advising competing firms can collude with no way to communicate, with no messages and no shared history. In a Cournot market, a model colludes if it picks the "cartel share," the output that's best for the firms together at buyers' expense. Across four levers (capability, caution, conscience and conceptualization), the stronger models always work out the cartel share but choose it strategically, mostly when collusion looks likely to last. Told that the rival gets the same prompt, many models anticipate each other's choices and collude even in a single round. A shared model and prompt can therefore act as the hub of a hub-and-spoke cartel without passing any information between firms. This collusion is fragile, though. One sentence naming a competition authority all but ends it for some models.

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Cite this project

@misc{carver2026four,
  title = {{Four Levers of LLM Collusion}},
  author = {Jason Carver and Thomas Redfern},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/four-levers-of-llm-collusion-o23m}},
  url = {https://apartresearch.com/sprints/projects/four-levers-of-llm-collusion-o23m}
}

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AI Collusion Research Sprint · Oct 23 - 25, 2026