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A Study of Heuristic Approaches for Solving Generalized Nash Equilibrium Problems and Related Games

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dc.contributor.advisor Cojocaru, Monica
dc.contributor.advisor Thommes, Edward Wild, Erin 2017-09-01T15:17:32Z 2017-09-01T15:17:32Z 2017-08 2017-08-28 2017-09-01
dc.description.abstract The use of various computational heuristics for solving generalized Nash equilibrium problems (GNEPs) and related games is explored. In a model of competitive helping, agent-based simulations are used as a complementary analysis tool in conjunction with replicator equations. These agent-based simulations highlight the emergence of behaviours as well as equilibrium amounts of help provided by individuals. Using a concept of Nash dominance, an evolutionary algorithm utilizing the Sierpinski representation was then developed to find representable solution sets for GNEPs in general. Following this is a comparison of two methods which attempt to find optimal strategies for playing a classic GNEP turned card game called deck-based divide-the-dollar. The first method uses evolutionary computation to evolve optimal players who are represented by binary decision automata. The second method uses Monte Carlo policy evaluation, a form of reinforcement learning, to iteratively optimize a player's strategy through experience with particular game states and eventual outcomes. The thesis concludes with some final remarks and suggestions for future work. en_US
dc.language.iso en en_US
dc.rights Attribution-NonCommercial 2.5 Canada *
dc.rights.uri *
dc.subject heuristics en_US
dc.subject game theory en_US
dc.subject optimization en_US
dc.subject agent-based modelling en_US
dc.subject evolutionary computation en_US
dc.subject competitive altruism en_US
dc.subject Monte Carlo methods en_US
dc.title A Study of Heuristic Approaches for Solving Generalized Nash Equilibrium Problems and Related Games en_US
dc.type Thesis en_US Mathematics and Statistics en_US Doctor of Philosophy en_US Department of Mathematics and Statistics en_US
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