Volume 120, Number 2, October 2017
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||08 January 2018|
Opinion dynamics in activity-driven networks
1 Institute of Applied Systems Analysis, Jiangsu University - Zhenjiang, Jiangsu 212013, China
2 College of Economics and Management, Nanjing University of Aeronautics and Astronautics Nanjing, Jiangsu 211106, China
3 School of Mathematical Science, Nanjing Normal University - Nanjing, Jiangsu 210042, China
4 Center for Polymer Studies and Department of Physics, Boston University - Boston, MA 02215, USA
Received: 4 October 2017
Accepted: 18 December 2017
Social interaction between individuals constantly affects the development of their personal opinions. Previous models such as the Deffuant model and the Hegselmann-Krause (HK) model have assumed that individuals only update their opinions after interacting with neighbors whose opinions are similar to their own. However, people are capable of communicating widely with all of their neighbors to gather their ideas and opinions, even if they encounter a number of opposing attitudes. We propose a model in which agents listen to the opinions of all their neighbors. Continuous opinion dynamics are investigated in activity-driven networks with a tolerance threshold. We study how the initial opinion distribution, tolerance threshold, opinion-updating speed, and activity rate affect the evolution of opinion. We find that when the initial fraction of positive opinion is small, all opinions become negative by the end of the simulation. As the initial fraction of positive opinions rises above a certain value —about 0.45— the final fraction of positive opinions sharply increases and eventually equals 1. Increased tolerance threshold δ is found to lead to a more varied final opinion distribution. We also find that if the negative opinion has an initial advantage, the final fraction of negative opinion increases and reaches its peak as the updating speed λ approaches 0.5. Finally we show that the lower the activity rate of individuals, the greater the fluctuation range of their opinions.
PACS: 89.75.-k – Complex systems / 89.75.Fb – Structures and organization in complex systems / 89.75.Hc – Networks and genealogical trees
© EPLA, 2018
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