TITLE:
Preana: Game Theory Based Prediction with Reinforcement Learning
AUTHORS:
Zahra Eftekhari, Shahram Rahimi
KEYWORDS:
Game Theory, Predictive Analytics, Reinforcement Learning
JOURNAL NAME:
Natural Science,
Vol.6 No.13,
August
25,
2014
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.