Dynamic Games and Applications

, Volume 1, Issue 1, pp 3–49 | Cite as

Opinion Dynamics and Learning in Social Networks

  • Daron AcemogluEmail author
  • Asuman Ozdaglar


We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society.


Bayesian updating Consensus Disagreement Learning Misinformation Non-Bayesian models Rule of thumb behavior Social networks 


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of EconomicsMITCambridgeUSA
  2. 2.Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science DepartmentMITCambridgeUSA

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