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.
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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@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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