Primal/Dual Positive Math Programming: Illustrated Through an Evaluation of the Impacts of Market Resistance to Genetically Modified Grains
扩展了Howitt的正数学规划方法，使其能同时设定原始和对偶变量，并揭示原方法的模糊性。通过一个均衡位移模型，评估美国贸易伙伴拒绝转基因作物对美国生产模式和农场净收入的影响。
Abstract The goal of Howitt's positive mathematical programming procedure is to calibrate a mathematical programming model so that it will reproduce a set of base data for the primal variables. This article develops an analogous procedure allowing one to specify the levels of both primal and dual variables. This article also sheds light on a potential ambiguity of Howitt's procedure (with attendant policy evaluation impacts). The procedure is illustrated through application to an equilibrium displacement model focused on evaluating the consequences of the reluctance of U.S. trading partners to accept genetically modified crop products for U.S. production patterns and net farm income.