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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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  1. The question is well posed as an evidence standard with each confounder analysed aiming to fool one type of signal, and the legal framing is detailed. However, the two set-ups are given different information depending on the shock, as elevation is measured against initial cost while the profit advantage is set under the true cost, which makes them substantially different settings. Also, legally any communication with acceptance is direct evidence of agreement, not a weak plus factor, so pure talk worlds would still be enforceable. Would be interesting to see whether collusion without talk in pure noise worlds to identify what is the core driver here, and to equalise the cost information for both signals.

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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