Opinion Dynamic

Opinion dynamics studies how individual opinions evolve within a group, driven by social interactions and external influences, aiming to understand consensus formation, polarization, and the spread of information. Current research focuses on refining models like the DeGroot and Hegselmann-Krause models, incorporating factors such as cognitive biases, network structure, and the impact of social media algorithms (including the use of LLMs for simulation), and exploring the use of novel techniques like variational inference for parameter estimation. This field is significant for understanding social phenomena like echo chambers and misinformation, informing the design of interventions to mitigate polarization, and improving the accuracy of agent-based models in social science.