Optimal influence design in networks ☆

• 成果类型: Article
• 署名作者: Jeong, Daeyoung; Shin, Euncheol
• 署名单位: Yonsei University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2024.105877
• 发表日期: 2024
• 关键词: Davis-Kahan sin Theta theorem; Singular value decomposition; social learning; social networks; Wedin sin Theta theorem

摘要

We examine an influence designer's optimal intervention in the presence of social learning in a network. Before learning begins, the designer alters initial opinions of agents within the network to shift their ultimate opinions to be as close as possible to the target opinions. By decomposing the influence matrix, which summarizes the learning structure, we transform the designer's problem into one with an orthogonal basis. This transformation allows us to characterize optimal interventions under complete information. We also demonstrate that even in cases where the designer has incomplete information about the network structure, the designer can still design an asymptotically optimal intervention in a large network. Finally, we provide examples and extensions, including repeated social learning and competition.