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Sprint projectOct 25, 2026Port Elizabeth, South Africa (Online)

What Should Count as Evidence of AI-Agent Collusion? Testing Plus-Factor Signals Against Innocent Look-Alikes

Larissa Engelbrecht

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

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Report: What Should Count as Evidence of AI-Agent Collusion? Testing Plus-Factor Signals Against Innocent Look-Alikes

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Detectors for AI-agent collusion often rely on similar behaviour or collusive-sounding messages, but innocent agents produce both. I borrowed antitrust law’s plus-factors doctrine, which holds that parallel conduct alone does not establish agreement, and turned it into five signals computed from agent logs. I tested them in a pricing market with three case types built to fool single signals: real collusion, price rises from a shared cost shock, and collusive talk without collusive action. With thresholds frozen on half the runs, talk-based rules falsely flagged up to 10 of 20 innocent runs, while a rule requiring price elevation plus coalition gain or causal dependence caught 10/10 colluders with no false positives. In these constructed cases, convincing evidence was behavioural, and it requires systems built to be audited.

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Cite this project

@misc{engelbrecht2026should,
  title = {{What Should Count as Evidence of AI-Agent Collusion? Testing Plus-Factor Signals Against Innocent Look-Alikes}},
  author = {Larissa Engelbrecht},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/what-should-count-as-evidence-of-aiagent-collusion-testing-plusfactor-signals-against-innocent-lookalikes-todz}},
  url = {https://apartresearch.com/sprints/projects/what-should-count-as-evidence-of-aiagent-collusion-testing-plusfactor-signals-against-innocent-lookalikes-todz}
}

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