When Is Society Susceptible to Manipulation?

• 成果类型: Article
• 署名作者: Mostagir, Mohamed; Ozdaglar, Asuman; Siderius, James
• 署名单位: University of Michigan System; University of Michigan; Massachusetts Institute of Technology (MIT)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4265
• 发表日期: 2022
• 页码: 7153-7175
• 关键词: Social networks; opinion dynamics; Behavioral models; strategic interventions

摘要

We consider a social learning model where agents learn about an underlying state of the world from individual observations as well as from exchanging information with each other. A principal (e.g., a firm or a government) interferes with the learning process in order to manipulate the beliefs of the agents. By utilizing the same forces that give rise to the wisdom of the crowd phenomenon, the principal can get the agents to take an action that is not necessarily optimal for them but is in the principal's best interest. We characterize the social norms and network structures that are susceptible to this kind of manipulation and derive conditions under which a social network is impervious and cannot be manipulated. In the process, we develop a new centrality measure and describe how our model offers insights into designing networks that are resistant to manipulation.