Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations...

We survey and synthesize models of network formation based on random graphs and stochastic processes. We organize the material according to whether the population is treated as fixed or dynamic. We pay particular attention to the effects of homophilous linking on network structure. Finally, we...

How and to what extent will new activities spread through social ties? Here, we develop a more sophisticated framework than the standard mean-field approach to describe the diffusion dynamics of multiple activities on complex networks. We show that the diffusion of multiple activities follows a...

We present an analytical solution for the connectivity of a network model with a "non-simultaneous" linking scheme. Despite its simplicity, this model exhibits node-space correlations in the link distribution, and anomalous fluctuations behavior of the time series of the connectivity variable,...

We present a new approach to the study of networks where the formation of links is driven by unilateral initiative of nodes. First, we propose a mathematical description of the extreme introvert and extrovert model (XIE), a dynamic network model in which the number of links fluctuates over time...

We model network formation when heterogeneous nodes enter sequentially and form connections through both random meetings and network-based search, but with type-dependent biases. We show that there is "long-run integration," whereby the composition of types in sufficiently old nodes'...

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...

We apply standardized numerical techniques of stochastic optimization (Judd [1998]) to the climate change issue. The model captures the feature that the effects of uncertainty are different with different levels of agent's risk aversion. A major finding is that the effects of stochasticity...