  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.
