Belief System Dynamics as Network of Single Layered Neural Network

As problems in political polarization and the spread of misinformation become serious, belief propagation on a social network becomes an important question to explore. Previous breakthroughs have been made in algorithmic approaches to understanding how group consensus or polarization can occur in a population. This paper proposed a modified model of the Friedkin-Johnsen model that tries to explain the underlying stubbornness of individual as well as possible back fire effect by treating each individual as a single layer neural network on a set of evidence for a particular statement with input being confidence level on each evidence, and belief of the statement is the output of this neural network. In this papar, we reafirmed the importance of Madison's cure for the mischief of faction, and found that when structure of understanding is polarized, a network with a giant component can decrease the variance in the belief distribution more than a network with two communities, but creates more social pressure by doing so. We also found that when community structure is formed, variance in the belief distribution become less sensitive to confidence level of individuals. The model can have various applications to political and historical problems caused by misinfomation and conflicting economic interest as well as applications to personality theory and behavior psychology.