  Many current challenges involve understanding the complex dynamical interplay
between the constituents of systems. Typically, the number of such constituents
is high, but only limited data sources on them are available. Conventional
dynamical models of complex systems are rarely mathematically tractable and
their numerical exploration suffers both from computational and data
limitations. Here we review generalized modeling, an alternative approach to
formulating dynamical models. We argue that this approach deals elegantly with
the uncertainties that exist in real world data and enables analytical insight
or highly efficient numerical investigation. We provide a survey of recent
successes of generalized modeling and a guide to the application of this
modeling approach in future studies such as complex integrative ecological
models.
