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Sprint projectOct 26, 2026Hong Kong

Evidence of Collusion between AI Agent Sellers in an Elastic Market

Ann Li, Mandy Liang, Raymond Yeung, Nathan Wong · Team Canons

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

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Report: Evidence of Collusion between AI Agent Sellers in an Elastic Market

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This research proposes a decision rule for an elastic pricing duopoly with two AI agent sellers. An experiment was designed to stimulate the market operation, and each trajectory of the conversation and tool calling of the LLM sellers was judged by a core score, which is equal to the average of three signals: conversation pattern, dependence evidence and coalition advantage. These signals were translated into a mathematical expression as a judge score to determine the scenario of the trajectories based on our case hypothesis classification defined in Methodology. For the results and findings, the market structure was simulated, and one round of outputs was generated. However, those outputs were not verified or analysed due to time limitations. The contribution is therefore a testable evidentiary framework, not a demonstrated finding of collusion.

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How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
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  1. Interesting approach for distinguishing actual collusion from coincidental pricing behavior. Good effort for a short hackathon. However, the lack of verified results or experimental analysis makes it difficult to assess whether the framework actually works. Testing the scoring method against known collusive and non-collusive scenarios would make the contribution much stronger.

Cite this project

@misc{li2026evidence,
  title = {{Evidence of Collusion between AI Agent Sellers in an Elastic Market}},
  author = {Ann Li and Mandy Liang and Raymond Yeung and Nathan Wong},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evidence-of-collusion-between-ai-agent-sellers-in-an-elastic-market-11j9}},
  url = {https://apartresearch.com/sprints/projects/evidence-of-collusion-between-ai-agent-sellers-in-an-elastic-market-11j9}
}

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