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We consider an extension of the Rescorla-Wagner model which bridges the gapbetween conditioning and learning on a neural-cognitive, individualpsychological level, and the social population level. In this model, theinteraction among individuals is captured by a Markov process. The resultinghuman-social behavior model is a recurrent iterated function systems whichbehaves differently from the classical Rescorla-Wagner model due to randomness.Convergence and ergodicity properties of the internal states of agents in theproposed model are studied.

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