发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现代市场的信息与机制设计问题，该研究在Bergemann等人的经济模型基础上，放宽卖家知晓买家品味信念的假设，通过FPTAS方案解决收益最大化问题，为不对称经济环境提供新学习视角。
AI 中文摘要
受现代市场的驱动，在这类市场中平台或卖家通常会收集详细的用户画像，我们研究一种同时涉及信息设计和机制设计的新型学习理论模型。具体而言，我们考虑Bergemann等人（2022）提出的经济环境，其中除了质量-价格对的菜单外，卖家还通过信号方案提供关于产品质量与买家品味匹配价值的信息。我们放宽了卖家知道买家对品味分布的信念这一假设，研究设计收益最大化方案的样本需求。我们既考虑可以访问一组独立同分布买家数据的批量设置，也考虑可以观察买家对卖家方案行为的在线需求查询模型。尽管该问题存在明显的非凸性，我们还给出了首个FPTAS（完全多项式时间近似方案），用于计算在任意小的加性损失内最大化收益的方案，这是Bergemann等人（2022）留下的未解决问题。总体而言，这为买卖双方掌握不同类型信息的不对称经济环境带来了新的学习视角。
英文摘要
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.
Comments29 Pages, 1 Table
