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Sprint projectOct 25, 2026

From Pairs To Coalitions: Extending Information-Theoretic Collusion Detection To Multi-Agent Games

Chae Eun Lee, Fabian Rosdalen, Peter Samodelkin, Yasin Edin · Team 29th-ML4Gang-Summit

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

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We study information-theoretic approaches to detect collusion and coalition members in a multi-player rock-paper-scissors game testbed with coordinated collusion. We compare a net-influence baseline with four methods that combine conditional mutual information, standardized against shuffled actions, with connected components, Louvain communities, likelihood-based search or average-$z$ growth. Across 2,800 coalition and 400 honest scripted test games, the ML decoder achieved the highest recall (81.71%), while Louvain most often recovered the exact coalition groups (73.29%). The graph methods could recover two separate coalitions that the single-group methods could not represent. Experiments with Qwen2.5:14B extend the evaluation to instructed model-generated coordination. The results motivate the use of action-based audits with information-theoretic approaches for detecting colluding agents.

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Cite this project

@misc{lee2026from,
  title = {{From Pairs To Coalitions: Extending Information-Theoretic Collusion Detection To Multi-Agent Games}},
  author = {Chae Eun Lee and Fabian Rosdalen and Peter Samodelkin and Yasin Edin},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/from-pairs-to-coalitions-extending-informationtheoretic-collusion-detection-to-multiagent-games-wqzo}},
  url = {https://apartresearch.com/sprints/projects/from-pairs-to-coalitions-extending-informationtheoretic-collusion-detection-to-multiagent-games-wqzo}
}

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