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Opinion Dynamics and Learning in Social Networks

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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Correspondence to Daron Acemoglu.

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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