From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms

• URL: http://arxiv.org/abs/2512.22749v1
• Date: Sun, 28 Dec 2025 02:41:36 GMT
• Title: From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
• Authors: Rui Ai, David Simchi-Levi, Feng Zhu,
• Abstract summary: We study the pricing behavior of third-party platforms facing strategic agents.<n>We develop an algorithm with optimal regret of $TildecO(sqrtTwedge_S-2)$.
• Score: 16.56794300689239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the pricing behavior of third-party platforms facing strategic agents. Assuming the platform is a revenue maximizer, it observes market features that generally affect demand. Since only the equilibrium price and quantity are observable, this presents a general demand learning problem under confounding. Mathematically, we develop an algorithm with optimal regret of $\Tilde{\cO}(\sqrt{T}\wedgeσ_S^{-2})$. Our results reveal that supply-side noise fundamentally affects the learnability of demand, leading to a phase transition in regret. Technically, we show that non-i.i.d. actions can serve as instrumental variables for learning demand. We also propose a novel homeomorphic construction that allows us to establish estimation bounds without assuming star-shapedness, providing the first efficiency guarantee for learning demand with deep neural networks. Finally, we demonstrate the practical applicability of our approach through simulations and real-world data from Zomato and Lyft.

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