A Modeling of Game Learning Theory Based on Fairness

Abstract

By incorporating fairness factor in the EWA (experience-weighted attraction) learning model, we develop an extended game learning model called FGL model. We use psychological effect in stead of material effect to modify strategy’s payoff and attraction, and to study the equilibrium movement further in dynamic Games. That participants have fair thinking will, in turn, lead to their psychological function changes. Compared with EWA learning model by simulating the decision-making in Ultimatum Game, we find FGL model converges to equilibrium strategy faster.