Verifiable communication on networks

• 成果类型: Article
• 署名作者: Gieczewski, German
• 署名单位: Princeton University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2022.105494
• 发表日期: 2022
• 关键词: Verifiable information; networks; social learning; strategic communication; Learning cascades

摘要

This paper models the diffusion of verifiable information on a network populated by biased agents. Some agents, who are exogenously informed, choose whether to inform their neighbors. Informing a neighbor affects her behavior, but also enables her to inform others. Agents cannot lie; they can, however, feign ignorance. The model yields three main results. First, unless a large set of agents is initially informed, learning is incomplete. Second, full learning is more likely for moderate than for extreme states of the world. Third, when agents are forward-looking, concerns about learning cascades lead to an endogenous division of the population into like-minded groups that do not communicate with each other. (c) 2022 Elsevier Inc. All rights reserved.