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Sprint projectOct 26, 2026Paris

Predicting False Collusion Alerts Under Incomplete Information: evidence from a simulated market and an exploratory re-asking of LLM agents.

Edimah SYNESIUS SONGO · Team Edimah

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

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Report: Predicting False Collusion Alerts Under Incomplete Information: evidence from a simulated market and an exploratory re-asking of LLM agents.

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We predicted and measured how missing shared information makes collusion detectors raise false alerts, and found that having more data can make it worse, and having stricter thresholds only delay it. Meanwhile, "re-asking" AI sellers offers an exploratory route with interesting preliminary results.

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Cite this project

@misc{songo2026predicting,
  title = {{Predicting False Collusion Alerts Under Incomplete Information: evidence from a simulated market and an exploratory re-asking of LLM agents.}},
  author = {Edimah SYNESIUS SONGO},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/predicting-false-collusion-alerts-under-incomplete-information-evidence-from-a-simulated-market-and-an-exploratory-reasking-of-llm-agents-ue2d}},
  url = {https://apartresearch.com/sprints/projects/predicting-false-collusion-alerts-under-incomplete-information-evidence-from-a-simulated-market-and-an-exploratory-reasking-of-llm-agents-ue2d}
}

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