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Opinion Dynamics and Learning in Social Networks

Dynamic Games and Applications volume 1, pages 3–49 (2011)Cite this article

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Economics, MIT, Cambridge, MA, 02139, USA

    Daron Acemoglu

  2. Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, MIT, Cambridge, MA, 02139, USA

    Asuman Ozdaglar

Authors
  1. Daron Acemoglu
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  2. Asuman Ozdaglar
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Correspondence to Daron Acemoglu.

Additional information

We thank Stefana Stantcheva for excellent research assistance.

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Acemoglu, D., Ozdaglar, A. Opinion Dynamics and Learning in Social Networks. Dyn Games Appl 1, 3–49 (2011). https://doi.org/10.1007/s13235-010-0004-1

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Keywords

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