Continued model-based decision support is associated with particular challenges, especially in long-term projects. Due to the regularly changing questions and the often changing understanding of the underlying system, the models used must be regularly re-evaluated, -modelled and -implemented with respect to changing modelling purpose, system boundaries and mapped causalities. Usually, this leads to models with continuously growing complexity and volume. In this work we aim to reevaluate the idea of the model family, dating back to the 1990s, and use it to promote this as a mindset in the creation of decision support frameworks in large research projects. The idea is to generally not develop and enhance a single standalone model, but to divide the research tasks into interacting smaller models which specifically correspond to the research question. This strategy comes with many advantages, which we explain using the example of a family of models for decision support in the COVID-19 crisis and corresponding success stories. We describe the individual models, explain their role within the family, and how they are used - individually and with each other.

翻译：持续的基于模型的决策支持面临着特殊挑战，尤其是在长期项目中。由于问题定期变化以及对底层系统认知的不断更新，所使用的模型必须根据不断变化的建模目标、系统边界和映射因果关系进行定期重新评估、重新建模和重新实现。这通常会导致模型的复杂性和规模持续增长。本研究旨在重新审视可追溯至20世纪90年代的“模型家族”理念，并将其推广为大型研究项目中构建决策支持框架的核心思维模式。其核心思想是：通常不开发和完善单个独立模型，而是将研究任务分解为多个相互关联的、针对特定研究问题的小型模型。这一策略具有诸多优势，我们通过COVID-19危机中用于决策支持的模型家族实例及相关成功案例加以说明。本文描述了各独立模型，阐释了它们在家族中的角色，以及它们如何单独或相互配合地发挥作用。