Asynchronous opinion dynamics on the k-nearest-neighbors graph

03/20/2018
by   Wilbert Samuel Rossi, et al.
0

This paper is about a new model of opinion dynamics with opinion-dependent connectivity. We assume that agents update their opinions asynchronously and that each agent's new opinion depends on the opinions of the k agents that are closest to it. We show that the resulting dynamics is substantially different from comparable models in the literature, such as bounded-confidence models. We study the equilibria of the dynamics, observing that they are robust to perturbations caused by the introduction of new agents. We also prove that if the number of agents n is smaller than 2k, the dynamics converge to consensus. This condition is only sufficient.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/12/2023

On a Voter Model with Context-Dependent Opinion Adoption

Opinion diffusion is a crucial phenomenon in social networks, often unde...
research
03/19/2020

Modeling limited attention in opinion dynamics by topological interactions

This work explores models of opinion dynamics with opinion-dependent con...
research
05/26/2019

Discrete Opinion Dynamics with M choices

Here, I study how to obtain an opinion dynamics model for the case where...
research
12/07/2018

Hierarchical Fuzzy Opinion Networks: Top-Down for Social Organizations and Bottom-Up for Election

A fuzzy opinion is a Gaussian fuzzy set with the center representing the...
research
12/12/2017

Robust Fragmentation Modeling of Hegselmann-Krause-Type Dynamics

In opinion dynamics, how to model the enduring fragmentation phenomenon ...
research
02/21/2020

Hysteresis and disorder-induced order in continuous kinetic-like opinion dynamics in complex networks

In this work we tackle a kinetic-like model of opinions dynamics in a ne...
research
01/22/2020

Dynamics of extended Schelling models

We explore extensions of Schelling's model of social dynamics, in which ...

Please sign up or login with your details

Forgot password? Click here to reset