用服务上限筛选用户紧迫度，实现信息服务利润最大化
Pricing Access to Dynamic Information Services
- 用单一无扭曲的信息流程，通过服务容量菜单筛选用户
- 容量越大，单位价格越低，符合经济直觉
- 适用于AI服务、专家咨询等按量收费场景
提供商销售一种动态信息服务——一种实时、容量受限的流程，用于消除客户不确定性——面向私人紧迫度不同的客户。本文刻画了收益最优机制：采用单一、未扭曲的信息流程（即无限访问客户最偏好的流程），并通过一维服务容量菜单完成筛选。提供商不扭曲产品本身，与穆萨-罗森垄断者不同；所有筛选都由容量限制承担。该机制解释了广泛存在的合同形式：固定费用 + 共享流程的限额访问，涵盖AI服务层级、专家网络咨询和分析师咨询续约。文章揭示了结果背后的经济动因：紧迫度筛选具有凸性保持特性，并给出单位价格随容量上升而下降的条件。
原文摘要 · Abstract (English)
A provider sells a \emph{dynamic information service}---a real-time, capacity-constrained process that resolves a customer's uncertainty---to customers who differ privately in urgency. I characterize the revenue-optimal mechanism: deploy a \emph{single}, undistorted information process---the one a customer with unlimited access would most prefer---and screen entirely through a one-dimensional menu of service caps. The provider leaves the product itself undistorted, unlike a Mussa--Rosen monopolist; all screening is absorbed into the cap. The mechanism rationalizes a recurring contractual form---a flat fee for capped access to a common process---spanning AI service tiers, expert-network consultations, and analyst-inquiry retainers. I identify the economic force behind the result, a convexity-preservation property of urgency screening, and give conditions under which the per-unit price declines in the cap.
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