Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks

We propose a new mathematical framework for the evolution and propagation of opinions, called fuzzy opinion network, which is the connection of a number of Gaussian nodes, possibly through some weighted average, time delay, or logic operators, where a Gaussian node is a Gaussian fuzzy set with the c...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2016-08, Vol.24 (4), p.880-905
Main Authors: Li-Xin Wang, Mendel, Jerry M.
Format: Article
Language:English
Subjects:
Bayes methods
Delays
Fuzzy sets
Mathematical model
Network theory (graphs)
opinion dynamics
social networks
Standard deviation
Uncertainty
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Summary:We propose a new mathematical framework for the evolution and propagation of opinions, called fuzzy opinion network, which is the connection of a number of Gaussian nodes, possibly through some weighted average, time delay, or logic operators, where a Gaussian node is a Gaussian fuzzy set with the center and the standard deviation being the node inputs and the fuzzy set itself being the node output. In this framework, an opinion is modeled as a Gaussian fuzzy set with the center representing the opinion itself and the standard deviation characterizing the uncertainty about the opinion. We study the basic connections of fuzzy opinion networks, including basic center, basic standard deviation (sdv), basic center-sdv, chain-in-center, and chain-in-sdv connections, and we analyze a number of dynamic connections to show how opinions and their uncertainties propagate and evolve across different network structures and scenarios. We explain what insights we might gain from these mathematical results about the formation and evolution of human opinions.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2015.2486816