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吕辰现为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、平台推荐等方面的应用。两篇研究成果发表于Journal of Economic Theory。

Agents arriving sequentially may adopt a new technology. Early adoption reveals information about its value, but individuals tend to wait and free-ride on information generated by others. Facing this challenge, we study how a designer—who controls the public release of past information—can improve social learning efficiency. The optimal design follows a threshold stopping rule, which keeps recommending adoption until the designer’s belief becomes more pessimistic than a time-varying threshold. This threshold typically starts above the first-best threshold, drops below it in some later periods, and eventually returns to it. Meanwhile, public information flow is restrained by over-recommending adoption, but not by under-recommending. We also examine special cases of conclusive-news learning. In good-news environments, the optimal design terminates exploration in a random manner; in bad-news environments, adoption may continue even after bad news arrives.