Preana: Game Theory Based Prediction with Reinforcement Learning

Zahra Eftekhari, Shahram Rahimi

Computer Science Department, Southern Illinois University Carbondale, Carbondale, USA.

DOI: 10.4236/ns.2014.613099                

Abstract

In this article, we have developed a game theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita’s algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players’ reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple steak-holders who have conflicting interests in economic, business, and political sciences. We have utilized game theory, expected utility theory, Median voter theory, probability distribution and reinforcement learning. We were able to reproduce Mesquita’s reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Irans 2013 presidential election to verify the accuracy of the prediction made by Preana.

Keywords

Game Theory, Predictive Analytics, Reinforcement Learning

Conflicts of Interest

The authors declare no conflicts of interest.

References

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