Preana: Game theory based prediction with reinforcement learning

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" Preana: Game theory based prediction with reinforcement learning "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	802961
Doc. No	:	TL48141
Call number	:	‭1660972150;‮ ‬1584818‬
Main Entry	:	Wadowiec, Jaime
Title & Author	:	Preana: Game theory based prediction with reinforcement learning
	:	\ Zahra Eftekhari
	:	Rahimi, Shahram
College	:	Southern Illinois University at Carbondale
Date	:	2014
Degree	:	M.S.
student score	:	2014
field of study	:	Computer Science
Page No	:	70
Note	:	Committee members: Carver, Norman; Mogharreban, Namdar
Note	:	Place of publication: United States, Ann Arbor; ISBN=978-1-321-59979-4
Abstract	:	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.
Subject	:	Computer science
Descriptor	:	Applied sciences;Expected utility theory;Game theory;Median voter theory;Predictive analysis;Reinforcement learning
Added Entry	:	Rahimi, Shahram
Added Entry	:	Southern Illinois University at Carbondale
	:	Computer Science