Limitations in agents’ ability to observe, measure and compute are studied. A formulation in terms of induced changes in the players’ subjective perception of the game makes it possible to study these phenomena on a common footing with objective variation in the game. We study a model where...

This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we...

Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques,...

Generalized power asymptotic expansions of solutions to differential equations that depend on parameters are investigated. The changing nature of these expansions as the parameters of the model cross critical values is discussed. An algorithm to identify these critical values and generate the...

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is mediated by a given intermediate variable (the indirect effect of the treatment), and the component that is not mediated...

We take the hierarchical resource allocation model in Van Zandt (2003a) and derive a simpler, reduced-form model of balanced hierarchies. This model uses continuous approximations; we derive bounds on the errors due to these approximations. We then give results that indicate that optimal...