Correlated default risk plays a significant role in financial markets. Dynamic intensity-based models, in which a firm default is governed by a stochastic intensity process, are widely used to model correlated default risk. The computations in these models can be performed by Monte Carlo...

We present a fully nonparametric method to estimate the value function, via simulation, in the context of expected infinite-horizon discounted rewards for Markov chains. Estimating such value functions plays an important role in approximate dynamic programming. We incorporate “soft...

This paper develops the first method for the exact simulation of reflected Brownian motion (RBM) with non-stationary drift and infinitesimal variance. The running time of generating exact samples of non-stationary RBM at any time t is uniformly bounded. The method can be used as a guide for...

Point processes with stochastic arrival intensities are ubiquitous in many areas, including finance, insurance, reliability, health care and queuing. They can be simulated from a Poisson process by time-scaling with the cumulative intensity. The paths of the cumulative intensity are often...

We examine the problem of allocating a resource repeatedly over time amongst a set of agents. The utility that each agent derives from consumption of the item is private information to that agent and, prior to consumption may be unknown to that agent. The problem is motivated by keyword...

We examine the problem of allocating a resource repeatedly over time amongst a set of agents. The utility that each agent derives from consumption of the item is private information to that agent and, prior to consumption may be unknown to that agent. The problem is motivated by keyword...

Waiting lists offer agents a choice between types of items with associated nonmonetary prices given by required waiting times. These nonmonetary prices are endogenously determined by a tâtonnement-like price discovery process: an item's price increases when an agent queues for it, and decreases...

We study a model of social learning with overlapping generations, where agents meet others and share data about an underlying state over time. We examine under what conditions the society will produce individuals with precise knowledge about the state of the world. Under the full information...

In the Bayesian online selection problem, the goal is to design a pricing scheme for a sequence of arriving buyers that maximizes the expected social-welfare (or revenue) subject to different types of structural constraints. Inspired by applications in operations management, the focus of this...