A Modeling of Game Learning Theory Based on Fairness

Qingquan He,Yulei Rao,Jie Xu

doi:10.5539/ijbm.v5n2p178

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.

Highlights

By incorporating fairness factor in the EWA learning model, we develop an extended game learning model called FGL model
This paper attempts to incorporate fairness factor into learning model to form the game learning model based on fairness
To verify the astringency and forecast ability of FGL model, we carry out a computer simulation to the ultimate game, and compare our results with the results simulated by EWA learning model

Summary

A Modeling of Game Learning Theory Based on Fairness

The project is supported by the National Natural Science Foundation of China(70872111),the Ministry of Education Post-project of Key Research Institute of Humanities and Social Science (06JHQZ0010) and Education Department Foundation of hunan Province(08c398)

The Compare of the Simulation to the Ultimatum Game

Conclusion