Aggregation of heterogeneous firms in mathematical programming models
针对数学规划部门模型中异质性企业加总时信息不全的问题，基于分解理论提出一种经验加总方法，帮助在缺乏完整信息时更准确地进行加总。
Serious aggregation errors may arise in mathematical programming sector models when aggregation is not properly done. Existing aggregation literature mostly focuses on exact aggregation of homogenous groups of firms. However, homogeneity rarely holds in real problem situations. Moreover, theoretical aggregation developments are generally based on the assumption of availability of full information about the firms to be aggregated. Such full information is hardly a plausible assumption when modelling the agricultural sector. This paper discusses an empirical aggregation approach based on decomposition theory which may be particularly helpful while aggregating heterogenous firms under the absence of full information.