Steady-state Analysis of a Neural-cognition Based Human-social Behavior Model

We consider an extension of the Rescorla-Wagner model which bridges the gap between conditioning and learning on a neural-cognitive, individual psychological level, and the social population level. In this model, the interaction among individuals is captured by a Markov process. The resulting human-social behavior model is a recurrent iterated function systems which behaves differently from the classical Rescorla-Wagner model due to randomness. Convergence and ergodicity properties of the internal states of agents in the proposed model are studied.