AI 中文总结
本文研究代理人视角下受可处理性约束的最优筛选机制，提出可搜索菜单要求，在多产品垄断等不同场景推导最优可搜索菜单形式，得出多维筛选中可搜索性限制可实施结果集合的结论。
AI 中文摘要
多维筛选是出了名的棘手问题。本文从代理人的角度研究受可处理性约束的最优筛选机制。具体而言，我们要求设计者提供的选项菜单可排序，使得无论代理人的偏好类型如何，都能通过贪心搜索找到效用最大化的选项：任何局部最优选择也必须是全局最优选择。在具有单交叉性质的一维筛选中，这一要求没有实际约束。然而在多维环境中，可搜索性限制了可实施结果的集合。在多产品垄断问题中，最优可搜索菜单是稀疏升级菜单：更高层级为每种商品提供更高的分配概率，且层级数量最多等于商品数量。在涉及金钱和磨难的多维筛选问题中，最优可搜索菜单为代理人提供获取商品的单一方式。在具有丰富多维异质性的所得税问题中，税收 schedule（税制）是可搜索的当且仅当它是累进的。
英文摘要
Multidimensional screening is (in)famously intractable. In this paper, we study optimal screening mechanisms subject to a tractability constraint from the agent's perspective. Specifically, we require that the menu of options offered by the designer can be ordered so that, regardless of her preference type, the agent can find a utility-maximizing option via greedy search: any locally optimal choice must also be globally optimal. In one-dimensional screening with the single-crossing property, this requirement has no bite. In multidimensional environments, however, searchability restricts the set of implementable outcomes. In the multiproduct monopoly problem, the optimal searchable menu is a sparse upgrade menu: higher tiers offer higher allocation probabilities for every good, and the number of tiers is at most the number of goods. In a multidimensional screening problem with money and ordeals, the optimal searchable menu offers the agent a single way to obtain the good. In income taxation with rich multidimensional heterogeneity, a tax schedule is searchable if and only if it is progressive.
Comments83 pages, 1 figure
