We consider an extension of the classical secretary problem where a decision maker observes only the relative ranks of a sequence of up to N applicants, whose true values are i.i.d. U[0,1] random variables. Applicants arrive according to a homogeneous Poisson Process, and the decision maker...

We develop a theory of optimal stopping problems under ambiguity in continuous time. Using results from (backward) stochastic calculus, we characterize the value function as the smallest (nonlinear) supermartingale dominating the payoff process. For Markovian models, we derive an adjusted...

We model and solve Best Choice Problems in the multiple prior framework: An ambiguity averse decision maker aims to choose the best among a fixed number of applicants that appear sequentially in a random order. The decision faces ambiguity about the probability that a candidate - a relatively...

We consider optimal stopping problems for ambiguity averse decision makers with multiple priors. In general, backward induction fails. If, however, the class of priors is time-consistent, we establish a generalization of the classical theory of optimal stopping. To this end, we develop first...

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent...

En la cub: "Revised version: September 2015" ; En este trabajo investigamos la interacción entre estabilidad de precios y sostenibilidad de la deuda soberana en el marco de un modelo de pequeña economía abierta, en la cual el gobierno emite deuda nominal y elige la política fi scal y...

This report describes methods for prioritizing the risk importances of maintenances using a Probabilistic Risk Assessment (PRA). Approaches then are described for quantifying their reliability and risk effects. Two different PRA importance measures, minimal cutset importances and risk reduction...

As the utility industry adapts to meet the changing regulatory and business climate, it is becoming increasingly important for utilities to identify and quantify the risks in various aspects of doing business. To reduce the risk of depending too heavily on one specific type of generation or...

A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by...

This paper investigates the questions of what statistical information about a memory request sequence is useful to have in making page replacement decisions: Our starting point is the Markov Request Model for page request sequences. Although the utility of modeling page request sequences by the...