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Modelling consensus and disagreements in social networks

He, Da
(2017)

Files

He_64741600_2017.pdf
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Details

Supervisors
Vannetelbosch, Vincent
Faculty
Faculté des sciences économiques, sociales, politiques et de communication
Degree label
Master [120] en sciences économiques, orientation économétrie
Abstract
This article surveys current research in major learning theories of social networks. The first part introduces convergence and learning in both Bayesian and non-Bayesian models. Particular attention is put on DeGroot model. Its micro-foundation and extensions are discussed in details. The second part addresses empirical evidences of dis-convergence and proposes non-stationarity to the benchmark DeGroot as an improvement. In addition, psychological findings on dynamic network structures are introduced.