The Application of Monte Carlo Sampling to Sequential Auction Games with Incomplete Information: An Empirical Study

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Title: The Application of Monte Carlo Sampling to Sequential Auction Games with Incomplete Information: An Empirical Study
Author: Zhu, Weili
Advisors: Dr. Peter Wurman, Chair
Dr. Michael Young, Member
Dr. Robert St. Amant, Member
Abstract: In this thesis, I develop a sequential auction model and design a bidding agent for it. This agent uses Monte Carlo sampling to 'learn' from a series sampled games. I use a game theory research toolset called GAMBIT to implement the model and collect some experimental data. The data shows the effect of different factors that impact on our agent's performance, such as the sample size, the depth of game tree, etc. The data also shows that our agent performs well compared with myopic strategic agent. I also discuss the possible relaxation of different aspects in our auction model, and future research directions.
Date: 2001-10-12
Degree: MS
Discipline: Computer Science
URI: http://www.lib.ncsu.edu/resolver/1840.16/1762


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