This thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A...

A model of employer advertised vacancies for labor is presented. The model's predictions are consistent with a variety of results reported by applied studies of the durations of different types of vacancies, and with the advertising strategies used by employers. The optimal advertising policy is...

This paper is concerned with finding an optimal inventory policy for a finite horizon, two-stage inventory model. In this model, products may be manufactured in two stages. The first stage uses raw material from an external supplier and the second stage uses raw material from defective items...

In this paper, we perform an in - depth investigation of relative merits of two adaptive learning algorithms with constant gain, Recursive Least Squares (RLS) and Stochastic Gradient (SG), using the Phelps model of monetary policy as a testing ground. The behavior of the two learning algorithms...

This paper studies how languages are shaped by the cognitive costs that using them involves. We introduce a new continuous approach to characterize the optimal resolution of the trade-off between the precision of a language and the complexity of the structures it uses, and its dependence on the...

An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I...

We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular we consider the convergence of policy gradient methods in the setting of known and unknown parameters. We are able to produce a global linear convergence...

This note provides a neat and enjoyable expansion and application of the magnificent Ordentlich-Cover theory of "universal portfolios". I generalize Cover's benchmark of the best constant-rebalanced portfolio (or 1-linear trading strategy) in hindsight by considering the best bilinear trading...

This paper proposes a new methodology to solve partially observed inventory problems. Generally, these problems have infinite dimensional states that are conditional distribution of the inventory level. Our methodology involves linearizing the state transitions via unnormalized probabilities. It...