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Sprint projectOct 26, 2026Cairo, Egypt

The Auditor’s Paradox: Evaluating Public and Private Anti-Collusion Warnings in LLM Pricing Markets

Yousef Mohamed Gabr, Youssef Azzam, Raya El Said · Team Eccentrics

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

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Report: The Auditor’s Paradox: Evaluating Public and Private Anti-Collusion Warnings in LLM Pricing Markets

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Automated anti-collusion monitors are designed to deter supracompetitive pricing, but their interventions fundamentally alter the strategic environment. We investigate the Auditor's Paradox: whether broadcasting a regulatory warning publicly acts as a focal point that inadvertently coordinates large language model (LLM) pricing agents. We simulated a repeated three-firm Calvano logit oligopoly across 191 completed trajectories (8,390 market rounds), comparing public, private, and silent warning policies, alongside separated opaque financial sanctions. Across 25 strict experimental cohorts, we found no statistically robust evidence that public warnings increase coordination; zero primary contrasts remained significant after Holm-Bonferroni correction. Forensic reconstruction revealed that apparent condition differences in standard evaluations are frequently artifacts of endogenous trigger sparsity and pre-warning stochastic divergence. Furthermore, analysing 16,128 chain-of-thought traces showed that while active notices dramatically increased agents' attention to regulatory oversight, they did not increase explicit collusive reasoning, instead often triggering defensive undercutting. These results highlight the methodological challenges of evaluating automated oversight and demonstrate that reliable AI monitoring tests require counterfactual branching from identical market states rather than unseeded intent-to-treat averages.

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Cite this project

@misc{gabr2026auditors,
  title = {{The Auditor’s Paradox: Evaluating Public and Private Anti-Collusion Warnings in LLM Pricing Markets}},
  author = {Yousef Mohamed Gabr and Youssef Azzam and Raya El Said},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-auditors-paradox-evaluating-public-and-private-anticollusion-warnings-in-llm-pricing-markets-ovjq}},
  url = {https://apartresearch.com/sprints/projects/the-auditors-paradox-evaluating-public-and-private-anticollusion-warnings-in-llm-pricing-markets-ovjq}
}

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