期刊
GAMES AND ECONOMIC BEHAVIOR
卷 76, 期 1, 页码 210-225出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.geb.2012.06.001
关键词
Social networks; Learning; Information aggregation
类别
资金
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [1147494] Funding Source: National Science Foundation
We develop a dynamic model of opinion formation in social networks when the information required for learning a parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true parameter. This result holds in spite of the apparent naivete of agents' updating rule, the agents' need for information from sources the existence of which they may not be aware of, worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate. (C) 2012 Elsevier Inc. All rights reserved.
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