`All models are wrong but some are useful' (George Box 1979). But, how to find those useful ones starting from an imperfect model? How to make informed data-driven decisions equipped with an imperfect model? These fundamental questions appear to be pervasive in virtually all empirical fields -- including economics, finance, marketing, healthcare, climate change, defense planning, and operations research. This article presents a modern approach (builds on two core ideas: abductive thinking and density-sharpening principle) and practical guidelines to tackle these issues in a systematic manner.
翻译:`所有模型都是错误的,但有一些是有用的'(George Box 1979年)。但是,如何从不完善的模式中找到这些有用的模型?如何作出由数据驱动的知情决定,并配以不完善的模式?这些基本问题似乎在几乎所有经验领域都普遍存在,包括经济、金融、营销、保健、气候变化、国防规划和行动研究,这篇文章提出了一种现代方法(建立在两个核心思想之上:诱拐思维和密度分散原则)和系统处理这些问题的实际准则。