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Sprint projectOct 26, 2026

Assessing the Reliability of AI-Agent Collusion Detection Across Games and Markets

Mélon Joanès AFAGNIBO, Xiaoyi CHEN, Hossam Fawzy, Solomon Midwinter · Team Think-Tank

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

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Report: Assessing the Reliability of AI-Agent Collusion Detection Across Games and Markets

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We test whether Pairwise Net Influence (NI), an information-theoretic collusion detector, identifies colluding AI agents better than action correlation and LLM judges, at a fixed 5% false-alarm rate. Across scripted Rock–Paper–Scissors, a pump-and-dump market and 75 published LLM double-auction runs, correlation wins when honest agents act independently, but fails completely (0% identification) when they herd on a shared public signal, where bias-corrected NI succeeds given enough data. We show that uncorrected NI has a pair-specific estimation bias that falsely implicates innocent pairs, and that repeated testing inflates false alarms to 29–34% unless calibrated over the whole sequence. LLM judges flag nearly every game and give unfaithful explanations; statistics improve their pair identification but not their false alarms. In real double-auction data, no action-only statistic detected the communication channel.

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Cite this project

@misc{afagnibo2026assessing,
  title = {{Assessing the Reliability of AI-Agent Collusion Detection Across Games and Markets}},
  author = {Mélon Joanès AFAGNIBO and Xiaoyi CHEN and Hossam Fawzy and Solomon Midwinter},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/assessing-the-reliability-of-aiagent-collusion-detection-across-games-and-markets-li06}},
  url = {https://apartresearch.com/sprints/projects/assessing-the-reliability-of-aiagent-collusion-detection-across-games-and-markets-li06}
}

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