Bounded Confidence Evolution of Opinions and Actions in Social Networks
Inspired by the continuous opinion and discrete action model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAE) model is proposed. In the SNOAE model, it is assumed that each agent has a continuous opinion and discrete action for a certain issue. Agents’ opinions are private and invisible, i.e. an individual agent only knows their own opinion and cannot obtain other agents’ opinions unless there is a social network connection edge that allows their communication; agents’ actions are public and visible to all agents and impact on other agents’ actions; and opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents’ actions or agents’ opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions upon. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Zhan, M., Kou, G., Dong, Y., Chiclana, F., Herrera-Viedma, E. (2021) Bounded Confidence Evolution of Opinions and Actions in Social Networks. IEEE Transactions on Cybernetics,
ISSN : 2168-2267
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes