We analyze optimal harvesting policy under stochastic price and stock dynamics. We state a set of weak conditions under which the optimal policy can be characterized by a single exercise threshold and show that the value of optimal harvesting and depletion policies can be expressed as the...

We develop a general methodology for a partially observed stochastic control problem. The dynamics is governed by a discrete-time Markov process. We describe an application to an inventory system with possibility of shrinkage, and introduce unnormalized conditional probabilities to transform the...

We consider a finite-horizon control model with additive input. There are two convex functions which describe the running cost and the terminal cost within the system. The cost of input is proportional to the input and can take both positive and negative values. It is shown that there exists a...

In this paper we develop several regression algorithms for solving general stochastic optimal control problems via Monte Carlo. This type of algorithms is particularly useful for problems with high-dimensional state space and complex dependence structure of the underlying Markov process with...

We examine a game of harvesting a renewable common resource and investigate the ramifications of competition. We formulate the problem as an impulse control game between two competing harvesters and devise a new formulation of closed-loop impulse control strategies. In the case of non-zero setup...