This paper explores the value of memory in decision making in dynamic environments. We examine the decision problem faced by an agent with bounded memory who receives a sequence of signals from a partially observable Markov decision process. We characterize environments in which the optimal...

We analyze investments in gas-fired power plants under stochastic electricity and natural gas prices. A simple but realistic two-factor model is used for price processes, enabling analysis of the value of operating flexibility, the opportunity to abandon the capital equipment, as well as finding...

The present project is related in the measurement of the risk and the improvement of the processes of monetary species. The main objective is to offer a methodology in the tickets administration because understanding of history is a situation that becomes in an advantage on the opportunities at...

The optimal stopping problem for the risk process with interests rates and when claims are covered immediately is considered. An insurance company receives premiums and pays out claims which have occured according to a renewal process and which have been recognized by them. The capital of the...

We analyze investments in gas-fired power plants under stochastic electricity and natural gas prices. A simple but realistic two-factor model is used for price processes, enabling analysis of the value of operating flexibility, the opportunity to abandon the capital equipment, as well as finding...

We give short proofs of general theorems about optimal entry and exit problems in Levy models, when payoff streams may have discontinuities and be non-monotone. As applications, we consider exit and entry problems in the theory of real options, and an entry problem with an embedded option to exit.

This paper deals with cognitive theories behind agent-based modeling of learning and information processing methodologies. Herein, I undertake a descriptive analysis of how human agents learn to select action and maximize their value function under reinforcement learning model. In doing so, I...

This paper deals with cognitive theories behind agent-based modeling of learning and information processing methodologies. Herein, I undertake a descriptive analysis of how human agents learn to select action and maximize their value function under reinforcement learning model. In doing so, I...

This paper deals with cognitive theories behind agent-based modeling of learning and information processing methodologies. Herein, I undertake a descriptive analysis of how human agents learn to select action and maximize their value function under reinforcement learning model. In doing so, I...

The class of defensive online portfolio selection algorithms,designed for fi nite investment horizon, is introduced. The Game Constantly Rebalanced Portfolio and the Worst Case Game Constantly Rebalanced Portfolio, are presented and theoretically analyzed. The analysis exploits the rich set of...