Social learning with multiple true states

In order to investigate social learning with multiple true states, a social learning model with time-varying topology and reliance weight is proposed. In this model, a time-varying topology mechanism for social networks is constructed since people always tend to communicate with those who have similar opinions with them. Simultaneously, the adaptive time-varying reliance weight mechanism is designed according to the closeness degree of agents’ neighbors. The simulation results show that asymptotic learning can be achieved and communities emerge under certain parameter values. Finally, how the parameters influence the belief evolution is analyzed, and a first order phase transition phenomenon is discovered.