动态调整平台服务费，解决数据混淆难题提升收益
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
- 利用非独立动作作为工具变量，破解需求学习中的混淆问题
- 理论证明算法最优误差为√T与σ_S⁻²的最小值，揭示供应噪声关键影响
- 适用于平台经济中策略性用户场景，尤其适合做定价优化的研究者
我们研究第三方平台在面对策略性用户时的定价行为。假设平台以收益最大化为目标，可观察影响需求的市场特征，但仅能获取成交数量与价格，形成典型的混淆下的需求学习问题。数学上，我们设计了最优后悔率˜O(√T ∧ σ_S⁻²) 的算法。结果表明，供给侧噪声从根本上影响需求可学习性，导致后悔率出现相变。技术上，我们证明非独立动作可作为工具变量用于需求学习；提出新颖的同胚构造，无需假设星形结构，首次为深度神经网络学习需求提供效率保证。最后，通过Talabat和Lyft的真实数据模拟与离线反事实分析，展示了该方法潜在的收益提升效果。
原文摘要 · Abstract (English)
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 transacted quantities and prices can be observed, this presents a general demand learning problem under confounding. Mathematically, we develop an algorithm with optimal regret of $\Tilde{\mathcal{O}}(\sqrt{T}\wedgeσ_S^{-2})$. 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 use simulations and offline counterfactuals from Talabat and Lyft data to illustrate the potential revenue implications of our approach.
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