发表机构
Stanford Graduate School of Business（斯坦福大学商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究开发基于对偶性的多维度筛选框架，应用于含固定核心项目的单参数族，通过两个阈值组织最优解，得出较高阈值有限性的分布条件为包容性。
AI 中文摘要
我开发了一个基于对偶性的多维度筛选框架，具有组合偏好的几何特征。对于一个机制要达到最优，类型分布确定了约束可行性约束的“所需”方向，而捆绑之间的互补性决定了“覆盖”方向；最优性归结为对所需方向的完全覆盖。我将该框架应用于一个单参数族，其中每个包含固定“核心”项目的捆绑都获得互补性溢价。两个阈值组织最优解：高于较低阈值必须提供大捆绑；高于较高阈值，一个“核心-外围”菜单——一个捆绑核心加上不单独出售的可选附加项——是最优的。较高阈值有限性的紧密分布条件是“包容性”，即菜单不排除任何接近顶部的买家。
英文摘要
I study a monopolist selling goods to a buyer with combinatorial preferences. The seller's dual is a minimum-cost flow of the differential virtual value. I characterize two reductions: when the differential virtual value is represented on types, and when this representation is independent of the preference. The distribution then determines the \emph{required directions}, along which the dual transports mass, and the preference the \emph{covered directions} of each bundle; optimality is full coverage. Comparative statics in the preference use this separation: a premium on bundles containing a core of goods models complementarity and widens their covered directions. Two thresholds describe the optimum: above a lower threshold the grand bundle must be offered; above an upper threshold a \emph{core-peripheral} menu --- a bundled core with optional add-ons not sold standalone --- is optimal. The upper threshold is finite if and only if the menu is \emph{inclusive}: it excludes no near-top buyer.
