卖家用用户画像和信号策略精准定价，提升收益。
Learning to Price with Persuasion
- 结合用户画像与信号机制设计动态定价方案
- 在独立同分布数据下实现接近最优的收益
- 首个可计算近似解法，适合平台经济研究者
受现代电商平台启发，本文研究一种融合信息与机制设计的学习理论模型。卖家不仅提供质量-价格组合，还通过信号机制向买家披露产品品质与个人偏好匹配度的信息。我们放宽了卖家需知买家信念分布的假设，分析在批量数据与在线查询两种场景下设计收益最大化方案的样本需求。尽管问题存在明显非凸性，本文首次提出一个固定参数近似算法（FPTAS），可实现任意小的附加误差下的收益最大化，解决了Bergemann等（2022）留下的开放问题。该工作为买卖双方信息不对称的经济场景提供了新的学习视角。
原文摘要 · Abstract (English)
Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer's taste via a signaling scheme. We relax the assumption that the seller knows the buyers' belief about the distribution of tastes and study the sample requirements of designing a revenue maximizing scheme. We consider both the batch setting where we have access to data from a set of i.i.d. buyers and an online demand query model where we observe the buyers' behaviors to seller's schemes. Despite the apparent non-convexity of the problem, we also give the first FPTAS to compute a scheme that maximizes the revenue within an arbitrarily small additive loss, which was left open by Bergemann et al. (2022). Overall, this brings a new learning perspective in asymmetric economic settings where buyers and sellers know different types of information.
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