Does aid cause conflict in Pakistan?

This study provides evidence from Pakistan on how the delegated task of achieving strategic objectives of the donor can lead to incompatibility of aid objectives which then generates perpetual and multidimensional domestic conflict in the recipient society. We use count data method to estimate the relationship between aid and conflict. At the aggregate level, social sector spending, regime change and youth bulge are positively and significantly related with conflict. However, aid per capita gives ambiguous results. It is significant with conflict count in the terrorism data-set and insignificant for data on armed conflict. Inclusion of youth bulge and unemployment rate confirms the marginalization hypothesis of conflict. Inflation rate and the tax variables are insignificant. This confirms that aid erodes fiscal capacity. At project-level data, conflict is strongly related with aid commitment and purpose. Discrepancy in aid allocation and commitment may accentuate conflict.     

Stochastic call center staffing with uncertain arrival,service and abandonment rates: A Bayesian perspective

In this article, we introduce staffing strategies for the Erlang‐A queuing system in call center operations with uncertain arrival, service, and abandonment rates. In doing so, we model the system rates using gamma distributions that create randomness in operating characteristics used in the optimization formulation. We divide the day into discrete time intervals where a simulation based stochastic programming method is used to determine staffing levels. More specifically, we develop a model to select the optimal number of agents required for a given time interval by minimizing an expected cost function, which consists of agent and abandonment (opportunity) costs, while considering the service quality requirements such as the delay probability. The objective function as well as the constraints in our formulation are random variables. The novelty of our approach is to introduce a solution method for the staffing of an operation where all three system rates (arrival, service, and abandonment) are random variables. We illustrate the use of the proposed model using both real and simulated call center data. In addition, we provide solution comparisons across different formulations, consider a dynamic extension, and discuss sensitivity implications of changing constraint upper bounds as well as prior hyper‐parameters. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 460–478, 2016     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.