Opinion Dynamics and Learning in Social Networks

By Daron Acemoglu, Asuman E. Ozdaglar. Dyn. Games Appl., 2011

742 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2297ee7a0866dedd2a4a9ca853c4215ba9507e03

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

A tutorial on modeling and analysis of dynamic social networks. Part I (2017, background): “not the least class of problems to be addressed deals with various problems of networks’ resilience against malicious and misbehaving agents, e.g. spreading rumors, fake news and other misinformation [296, 297] or propagating extremist ideas. Identiﬁcation of the most inﬂuential individuals (e.g. mo”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “Much ongoing interest is focusing on dynamic models of structural change [1] [25] [43] [54] [70], and on a broad range of dynamic processes unfolding over static networks; examples include the study of social learning [2] [35], opinion formation [23] [32] and information propagation [25] [57] [60].”
Adaptation, Learning, and Optimization over Networks (2014, background): “Examples of references that discuss applications of consensus implementations include [2, 18, 64, 80, 118, 122, 123, 181, 184, 185, 199, 200, 255].”
Manufacturing Consent (2015, background): “, social impact [11], bounded confidence [8], Bayesian learning [12].”
Binary Opinion Dynamics with Stubborn Agents (2013, methodology): “Instead, most societies appear to exhibit persistent disagreement, even on topics on which there is frequent communication (see [Acemoglu and Ozdaglar 2010] for a recent survey). In this article, we investigate a model of binary opinion dynamics based on the well-known voter model originally develop”