  Scientists investigate the dynamics of complex systems with quantitative
models, employing them to synthesize knowledge, to explain observations, and to
forecast future system behavior. Complete specification of systems is
impossible, so models must be simplified abstractions. Thus, the art of
modeling involves deciding which system elements to include and determining how
they should be represented. We view modeling as search through a space of
candidate models that is guided by model objectives, theoretical knowledge, and
empirical data. In this contribution, we introduce a method for representing
process-based models that facilitates the discovery of models that explain
observed behavior. This representation casts dynamic systems as interacting
sets of processes that act on entities. Using this approach, a modeler first
encodes relevant ecological knowledge into a library of generic entities and
processes, then instantiates these theoretical components, and finally
assembles candidate models from these elements. We illustrate this methodology
with a model of the Ross Sea ecosystem.
