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Sprint projectOct 26, 2026

WHICH RIVAL MAKES A PRICE CUT UNPROFITABLE? COUNTERFACTUAL DETERRENCE AUDITS OF LLM PRICING AGENTS

Drishti Sharma · Team DriWorks

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

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Report: WHICH RIVAL MAKES A PRICE CUT UNPROFITABLE? COUNTERFACTUAL DETERRENCE AUDITS OF LLM PRICING AGENTS

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High prices alone do not establish whether competitors deter undercutting. We study two counterfactual pricing experiments with DeepSeek V4.1 Flash and Qwen3.8 Flash. The first compares a prescribed price cut with matched no-cut continuations while neither, either, or both rivals respond. Across eight completed markets, five contained a qualifying profitable cut. A single rival’s response made the cut unprofitable in four of those five markets; none exhibited deterrence requiring both rivals. The second experiment removes scripted retaliation while retaining or resetting the seller’s context. With context retained, removal increased mean prices by 0.082 for DeepSeek across eight calm-checkpoint pairs and 0.060 for Qwen across seven pairs. DeepSeek continued undercutting despite retaliation, making its observed adjustment consistent with responding to a higher-priced rival rather than learned deterrence. Scripted positive and negative controls validate the measurement pipeline. These small-sample findings concern specific interventions and selected states, not intentions or real-world collusion.

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  1. The strongest part is separating individual rival effects from their combined response. The experiments are useful, and the authors are careful not to treat every response as evidence of collusion. My main concern is the small sample: only five games had profitable cuts to test, and ordinary competitive responses could explain the apparent deterrence. Repeating the experiments with different cut sizes and making the code public would strengthen the findings. The abstract could also do a better job of explaining the main takeaway upfront.

Cite this project

@misc{sharma2026which,
  title = {{WHICH RIVAL MAKES A PRICE CUT UNPROFITABLE? COUNTERFACTUAL DETERRENCE AUDITS OF LLM PRICING AGENTS}},
  author = {Drishti Sharma},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/which-rival-makes-a-price-cut-unprofitable-counterfactual-deterrence-audits-of-llm-pricing-agents-qipa}},
  url = {https://apartresearch.com/sprints/projects/which-rival-makes-a-price-cut-unprofitable-counterfactual-deterrence-audits-of-llm-pricing-agents-qipa}
}

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