Skip to content
Apart Research
Apart Research
Contact us
Sprint projectOct 26, 2026Los Angeles, California

Trading on the Margins: Independent Actions, Correlated Judgement, and the Limits of AI Collusion Detection in Prediction Markets

Katrina Gacusan Manrique · Team N/A - Individual Submission

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

Read the report

Report: Trading on the Margins: Independent Actions, Correlated Judgement, and the Limits of AI Collusion Detection in Prediction Markets

Open in a new tab (opens in new tab)
Recording (opens in new tab)Code (opens in new tab)
Share

Prediction markets rely on independent trading decisions to produce reliable forecasts, but what happens when multiple traders use the same AI model and instructions? This study investigates whether AI agents instructed to favor the same candidate engage in tacit collusion and whether awareness of other agents with the same goal changes their behavior. I ran 275 simulated election markets across 17 conditions using Claude Haiku 5.5 agents, varying their instructions, awareness of other traders, and Candidate A’s true winning probability (30% or 10%). Agents sacrificed potential earnings to support A, but awareness of others sharing their goal did not significantly increase financial sacrifice (−0.17 percentage points, 95% CI −0.36 to 0.01) or price distortion. Explicit coordination instructions, however, increased financial sacrifice by 1.25 percentage points. More importantly, an exploratory analysis found that when A’s true winning probability was 10%, markets with three pro-A agents overestimated it by approximately 5 percentage points, compared with 0.2 points in all-neutral markets, regardless of mutual awareness. These findings revealed that agents independently following identical instructions can collectively distort forecasts. More broadly, this study offers a proof of concept for using AI’s consistent instruction-following to expose latent systemic vulnerabilities that individual agent alignment cannot address or solve on its own.

Reviews

Judging this Sprint?

Review “Trading on the Margins: Independent Actions, Correlated Judgement, and the Limits of AI Collusion Detection in Prediction Markets”

Your public critique appears on the project page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on the project page.

Shown on the project page, without your name.

Only the Apart team reads this, and funders if you agree below.

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; the project page shows only that a judge declined, not why.

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.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

See our Privacy Policy.

  1. Pre-registered study with a clear positive control. Agents told to back Candidate A gave up money to distort a 10% market by about 5 points, without coordinating. The title promises detection limits, but no detector is run. Run one, and separate the shared model from the shared instruction.

Cite this project

@misc{manrique2026trading,
  title = {{Trading on the Margins: Independent Actions, Correlated Judgement, and the Limits of AI Collusion Detection in Prediction Markets}},
  author = {Katrina Gacusan Manrique},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/trading-on-the-margins-independent-actions-correlated-judgement-and-the-limits-of-ai-collusion-detection-in-prediction-markets-qucs}},
  url = {https://apartresearch.com/sprints/projects/trading-on-the-margins-independent-actions-correlated-judgement-and-the-limits-of-ai-collusion-detection-in-prediction-markets-qucs}
}

More from AI Collusion Research Sprint

All 392 entriesBrowse all projects

A Sprint is running now

AI x Epistemics Research Sprint · Nov 13 - 15, 2026