讲座题目：Information Design for Social Learning with Patient Agents（面向有耐心群体社会学习的信息设计）

主讲人：吕辰  北京大学汇丰商学院

讲座时间：2025年10月27日10:00

讲座地点：学院210

讲座内容摘要：

Sequentially coming agents may adopt a new technology. Early adoption can generate information about its value, which is either high or low, and an intermediary decides how such information will be dynamically published. Because individuals tend to wait and free-ride on information generated by others, efficient social learning is hard to achieve. Facing this challenge, we study how the intermediary can improve social welfare by designing its information publishing policy. To incentivize early adoption, we show it is optimal to restrain future information flow via inducing individually sub-optimal adoption but not excessive waiting. The optimal design features a simple threshold stopping structure: in every period, recommend adoption if the intermediary’ s current belief is more optimistic than a threshold; otherwise, recommend waiting forever. While the first-best design uses a constant threshold, the optimal design features time-varying thresholds that typically cross the first-best one. We also examine special cases where learning is via conclusive news. In the good news environment, the optimal design needs to involve a middle phase in which exploration is randomly terminated; in the bad news environment, adoption may be continued even if bad news has arrived. These serve to mitigate the individuals’ incentive problem efficiently.

序贯决策的个体可能选择采纳某项新技术。早期采纳行为能够产生关于技术价值（高或低）的信息，而信息中介可以动态控制这些信息的发布。由于个体倾向于等待并搭便车利用他人产生的信息，实现有效社会学习面临挑战。针对这一问题，我们研究信息中介如何通过设计信息发布策略来提升社会福利。为激励早期采纳，最优策略会通过引导个体实施次优采纳，而非过度等待，来限制未来信息流。最优设计具有简单的阈值停止结构：在每个决策期，若中介当前信念高于阈值则推荐采纳；否则建议永久等待。虽然首优设计采用恒定阈值，但最优设计采用随时间变化的阈值，且通常会跨越首优阈值。我们还考察了通过决定性消息进行学习的特殊情形：在好消息环境中，最优设计需要包含随机终止探索的中间阶段；在坏消息环境中，即使出现坏消息仍可能建议继续采纳。这些机制能有效缓解个体激励问题。

主讲人学术简介：

吕辰，北京大学汇丰商学院助理教授。主要从事信息经济学相关的应用微观理论研究，重点关注信息设计及其在产业组织、平台设计等方面的应用。研究成果发表于Journal of Economic Theory。