Dynamics Analysis for the Random Homogeneous Biased Assimilation Model

This paper studies the evolution of opinions over random social networks subject to individual biases. An agent reviews opinion a randomly selected one and then updates its under homogeneous biased assimilation. study investigates impact assimilation on networks, which is different from previous fixed network structures. If bias parameters are static, it proven that event in all agents converge extreme happens almost surely. Next, polarization proved be probability event. While if dynamic, depend early finite time slots for dynamical parameter functions independent values after threshold. Numerical simulations further show depends some nonlinear functions.