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

2025-12-27

AI Summary

研究第三方平台如何动态定价服务费以应对战略代理商。通过开发具有最优后悔的算法，利用非独立同分布行动作为工具变量学习需求，并在实际数据中验证了方法的有效性。

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Dynamic service fee pricing on third-party platforms with strategic agents.
• Demand learning under confounding from equilibrium price and quantity.
• Supply-side noise affecting learnability and causing regret phase transition.

Innovation

• Algorithm with optimal regret for demand learning
• Non-i.i.d. actions as instrumental variables
• Homeomorphic construction for neural network efficiency