Passing the Placebo Is Not Enough: Testing LLM Pricing-Collusion Detectors Against Known Strategies
Diego Krumdieck
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
A competitive pricing agent can respond to its rival without colluding, and agents built on the same model can move together without responding at all. We test whether Fish et al.’s lagged-price coefficient δ, which they read as a sign of reward–punishment, and related tests distinguish collusion from competition. Using 37 duopoly games with Claude Haiku 5.5 (21 baseline, six instructed to keep prices high, ten given an explicit punishment strategy), we compare real seller pairs with placebo pairs from different games and test detectors against rule-based agents with known strategies.
The baseline δ exceeds its placebo mean (0.086 versus 0.005; p = 0.001) but is not significant with game-clustered standard errors. It also flags a competitive follower, misses our rule-based colluder at four punishment lengths, and cannot be estimated in the six games told to keep prices high, where sellers stop moving. A one-period undercut followed by restoration of the rival’s price separates the follower from punishers when observed long enough. In LLM games, ten of twelve still below control at period 220 were also below at 260, including three given the punishment strategy; such endpoints cannot distinguish persistent punishment from a price adjustment followed by freezing. Passing a placebo test does not establish collusion. We recommend validating detectors against noninteracting sellers and known strategies, and using interventions long enough to observe recovery.
Reviews
- The paper proposes a clear standard: a detector must beat a placebo and also separate agents with known strategies
- It is dense. Too many conditions, categories and caveats compete for attention. The two-step standard should come first, with the fork details in an appendix.
This paper asks the right question: when a test for collusion reports a positive result, should we believe it? The author checks a widely used measure - how much one agent's price follows its rival's previous price - in two ways I found convincing. First, against placebo pairs: sellers taken from different games, who never actually competed. Second, against simple rule-based agents whose strategy is known, one that just follows its rival competitively and one that genuinely punishes price cuts.
The result is a warning. The measure beats the placebo, which looks reassuring, but it also flags the competitive follower as colluding and misses the real colluder at every punishment length tried. In other words, passing the placebo test is not enough. The paper also tries a more direct approach - forcing one agent to undercut and watching what happens next - which separates followers from punishers much better, as long as you watch long enough. With the LLM agents, many simply cut their price and never came back, even when they had been told to, which makes long observation windows essential.
Two suggestions: try a wider range of colluder designs, since only one style of punisher was tested, and add a simple summary of how each test performs on the known cases, so auditors can compare them at a glance.
Overall, a careful paper that raises the standard for what should count as evidence of collusion.
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Cite this project
@misc{krumdieck2026passing,
title = {{Passing the Placebo Is Not Enough: Testing LLM Pricing-Collusion Detectors Against Known Strategies}},
author = {Diego Krumdieck},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/passing-the-placebo-is-not-enough-testing-llm-pricingcollusion-detectors-against-known-strategies-zrv8}},
url = {https://apartresearch.com/sprints/projects/passing-the-placebo-is-not-enough-testing-llm-pricingcollusion-detectors-against-known-strategies-zrv8}
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