The Impacts of WTO and Water Policy Changes on Saudi Arabian Agriculture: Results from an Equilibrium Displacement Model

Saudi Arabia's food consumption has grown dramatically over time. There has been a sharp increase in food consumption and significant changes in the composition of food consumed. Therefore, it is important that the government of Saudi Arabia anticipate further effects of these changes on growth of food demand and focus on food policies that contribute to development goals. On the other hand, limited agricultural productivity and the nature of the country's climatic conditions have constrained agricultural production. This restricted growth in production, combined with population growth, has led Saudi Arabia to depend heavily on food imports to cover the gap between domestic demand and local production. The increased reliance on imports as a source of food will increase the country's import demand. These main problems facing the Saudi agricultural sector suggest the need for an analytical framework that can evaluate effects of policy and resource change on imports, local production and local demand simultaneously. Ideally, the framework should account for substitution and income effects across products that might arise from changes in consumption and production patterns. This is the main objective of this paper.Food Consumption/Nutrition/Food Safety,

A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly bound the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.Comment: 8 pages, 4 figures, 2 pages reference