On the modeling of neural cognition for social network applications

TL;DR

This paper introduces a stochastic neural cognition model for social networks that unifies existing models, analyzes stability, and demonstrates convergence to consensus under different disturbance conditions.

Contribution

It presents a new stochastic model that encompasses Pavlovian and social network models, analyzing stability and consensus in dynamic social network scenarios.

Findings

The model is mean square stable under various disturbance conditions.

The system's states converge to consensus over time.

The model unifies well-known social and conditioning models.

Abstract

In this paper, we study neural cognition in social network. A stochastic model is introduced and shown to incorporate two well-known models in Pavlovian conditioning and social networks as special case, namely Rescorla-Wagner model and Friedkin-Johnsen model. The interpretation and comparison of these model are discussed. We consider two cases when the disturbance is independent identical distributed for all time and when the distribution of the random variable evolves according to a markov chain. We show that the systems for both cases are mean square stable and the expectation of the states converges to consensus.