DOI
https://doi.org/10.25772/TE7E-WE38
Defense Date
2006
Document Type
Thesis
Degree Name
Master of Science
Department
Mathematical Sciences
First Advisor
Dr. Hassan Sedaghat
Abstract
Problems in statistical analysis, economics, and many other disciplines often involve a trade-off between rewards and additional information that could yield higher future rewards. This thesis investigates such a trade-off, using a class of problems known as bandit problems. In these problems, a reward-seeking agent makes decisions based upon his beliefs about a parameter that controls rewards. While some choices may generate higher short-term rewards, other choices may provide information that allows the agent to learn about the parameter, thereby potentially increasing future rewards. Learning occurs if the agent's subjective beliefs about the parameter converge over time to the parameter's true value. However, depending upon the environment, learning may or may not be optimal, as in the end, the agent cares about maximizing rewards and not necessarily learning the true value of the underlying parameter.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
June 2008