The relative contributions of private information sharing and public information releases to information aggregation

• 成果类型: Article
• 署名作者: Duffie, Darrell; Malamud, Semyon; Manso, Gustavo
• 署名单位: Stanford University; Swiss Finance Institute (SFI); Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Massachusetts Institute of Technology (MIT)
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2009.10.017
• 发表日期: 2010
• 页码: 1574-1601
• 关键词: Information percolation; search; Learning rates

摘要

We calculate learning rates when agents are informed through public and private observation of other agents' actions. We characterize the evolution of the distribution of posterior beliefs. If the private learning channel is present, convergence of the distribution of beliefs to the perfect-information limit is exponential at a rate equal to the sum of the mean arrival rate of public information and the mean rate at which individual agents are randomly matched with other agents. If, however, there is no private information sharing, then convergence is exponential at a rate strictly lower than the mean arrival rate of public information. (C) 2009 Elsevier Inc. All rights reserved.