Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’

There is an emerging strand in the agricultural economics literature which examines the calibration of risk programming models using the principles of Positive Mathematical Programming (PMP). In a recent contribution to this journal, Liu et al. (2020) compare three different PMP approaches and attempt to find the ‘most practical’ method for calibrating risk programming models to be used in policy analysis. In this article, we argue that the comparison design by Liu et al. (2020) is problematic, as it is based on inappropriate metrics and it ignores recent advancements in PMP. This word of caution intends to provide constructive criticism and aims at contributing to the use of risk programming models in policy analysis.农业经济学领域的文献中正兴起一类研究分支，该分支依托正数学规划（Positive Mathematical Programming, PMP）原理开展风险规划模型的校准研究。本刊近期刊载的一篇研究论文中，Liu等人（2020）对比了三种不同的PMP方法，并试图探寻可用于政策分析的风险规划模型校准的"最实用"方法。本文认为，Liu等人（2020）的对比研究设计存在缺陷：其采用了不恰当的评价指标，且忽视了PMP领域近期的研究进展。本文特此提出此警示，旨在给出建设性批评，以期推动风险规划模型在政策分析中的应用。