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

TL;DR

This paper extends the Rescorla-Wagner model to include social interactions using a Markov process, analyzing its convergence and ergodic properties to better understand neural-cognitive and social behavior dynamics.

Contribution

It introduces a novel social behavior model combining neural-cognitive and population-level dynamics, with a focus on stochastic interactions and their long-term properties.

Findings

The model exhibits convergence of internal states.

The model demonstrates ergodicity under certain conditions.

Behavior differs from classical models due to randomness.

Abstract

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.