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

TL;DR

This paper introduces Fuzzy Opinion Networks, a mathematical framework modeling opinion evolution and uncertainty propagation across social networks using Gaussian fuzzy sets and various connection types.

Contribution

It develops a novel mathematical framework for modeling opinion dynamics and uncertainties in social networks using Gaussian fuzzy sets and network connections.

Findings

Analyzes how opinions and uncertainties propagate in different network structures.

Provides insights into opinion formation and evolution.

Defines basic and dynamic connection models for opinion networks.

Abstract

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.