Abstract
In this study, we present a computational model for simulating opinion dynamics within social networks, incorporating cognitive and social psychological principles such as homophily, confirmation bias, and selective exposure. We enhance our model using Dempster–Shafer theory to address uncertainties in belief updating. Mathematical formalism and simulations were performed to derive empirical results from showcasing how this method might be useful for modeling real-world opinion consensus and fragmentation. By constructing a scale-free network, we assign initial opinions and iteratively update them based on neighbor influences and belief masses. Lastly, we examine how the presence of “truth” nodes with high connectivity, used to simulate the influence of objective truth on the network, alters opinions. Our simulations reveal insights into the formation of opinion clusters, the role of cognitive biases, and the impact of uncertainty on belief evolution, providing a robust framework for understanding complex opinion dynamics in social systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
