  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.
