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互动最大化

Engagement maximization
Theoretical Economics · 2026 · Benjamin M. Hebert、Weijie Zhong

中文摘要

我们研究信息提供的最优管理以实现用户互动最大化。一个委托方序贯地向一个拥有有限信息处理能力、并可随时选择退出的代理人揭示信号。我们识别出一种“稀释”策略——发送稀少但信息量极高的信号——能够实现用户互动最大化。相对于用户最优基准,平台的互动指标塑造了所提供信息中偏误的方向与幅度。即使在不存在跨期承诺的情况下,平台也能通过诱导用户信念在稀有且决定性的信号到来并引发停止之前始终保持“与”先验“同等不确定”,从而复制完全承诺下的收益。我们将研究结果应用于两个情境:一个广告支持的互联网媒体平台,以及一位试图吸引应试动机学生的教师。

Abstract

We investigate the management of information provision to maximize user engagement. A principal sequentially reveals signals to an agent who has a limited amount of information processing capacity and can choose to exit at any time. We identify a “dilution” strategy—sending rare but highly informative signals—that maximizes user engagement. The platform's engagement metric shapes the direction and magnitude of biases in provided information relative to a user‐optimal benchmark. Even without intertemporal commitment, the platform replicates full‐commitment revenue by inducing the user's belief to remain “as uncertain as” the prior until the rare, decisive signal arrives and induces stopping. We apply our results to two contexts: an ad‐supported internet media platform and a teacher attempting to engage test‐motivated students.
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