Information diffusion in networks with the Bayesian Peer Influence heuristic

• 成果类型: Article
• 署名作者: Levy, Gilat; Razin, Ronny
• 署名单位: University of London; London School Economics & Political Science
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2017.12.020
• 发表日期: 2018
• 页码: 262-270
• 关键词: correlation neglect; Learning in networks; Bayesian heuristic; Polarisation

摘要

Repeated communication in networks is often considered to impose large information requirements on individuals, and for that reason, the literature has resorted to use heuristics, such as DeGroot's, to compute how individuals update beliefs. In this paper we propose a new heuristic which we term the Bayesian Peer Influence (BPI) heuristic. The BPI accords with Bayesian updating for all (conditionally) independent information structures. More generally, the BPI can be used to analyze the effects of correlation neglect on communication in networks. We analyze the evolution of beliefs and show that the limit is a simple extension of the BPI and parameters of the network structure. We also show that consensus in society might change dynamically, and that beliefs might become polarised. These results contrast with those obtained in papers that have used the DeGroot heuristic. (C) 2018 Elsevier Inc. All rights reserved.