Discrete-Time Polar Opinion Dynamics with Susceptibility

TL;DR

This paper introduces a discrete-time opinion dynamics model where individuals' susceptibility to influence depends on their current opinions, extending existing models to include opinion-dependent susceptibility functions and analyzing conditions for consensus.

Contribution

The paper develops a novel opinion dynamics model with opinion-dependent susceptibility functions, generalizes the DeGroot and Friedkin-Johnsen models, and analyzes conditions for consensus and extremal opinions.

Findings

Consensus conditions depend on initial opinions and network topology.

Opinion susceptibility functions influence whether opinions reach consensus or extremal values.

Simulations demonstrate the impact of susceptibility functions compared to traditional models.

Abstract

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.