In this paper we examine models that incorporate CBDT. In the first chapter, we will examine CBDT more thoroughly including a reinterpretation of the standard labor supply problem under a wage tax in a partial equilibrium model where preferences exhibit characteristics of CBDT. In the second chapter, we extend the labor supply decision under a wage tax by incorporating a household production function. Utility maximization by repeated substitution is applied as a novel approach to solving dynamic optimization problems. This approach allows us to find labor supply elasticities that evolve over the life cycle. In the third chapter, CBDT will be explored in more depth focusing on its applicability in representing people's preferences over movie rentals in the Netflix competition. This chapter builds on the theoretical model introduced in chapter 1, among other things, expressing the rating of any customer movie pair using the ratings of similar movies that the customer rated and the ratings of the movie in question by similar customers. We will also explore in detail the econometric model used in the Netflix competition which utilizes machine learning and spatial regression to estimate customer's preferences.

This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-of of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine's capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work. / Ph. D.