Return to search

Preana: Game-theory Based Prediction with Reinforcement Learning

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 stake-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 Iran's 2013 presidential election to verify the accuracy of the prediction made by Preana.

Identiferoai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-2566
Date01 December 2014
CreatorsEftekhari, Zahra
PublisherOpenSIUC
Source SetsSouthern Illinois University Carbondale
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses

Page generated in 0.002 seconds