Assembling ensembling: An adventure in approaches across disciplines

When we think of model ensembling or ensemble modeling, there are many
possibilities that come to mind in different disciplines. For example, one
might think of a set of descriptions of a phenomenon in the world, perhaps
a time series or a snapshot of multivariate space, and perhaps that set is
comprised of data-independent descriptions, or perhaps it is quite
intentionally fit to data, or even a suite of data sets with a common
theme or intention. The very meaning of ‘ensemble’ - a collection together
- conjures different ideas across and even within disciplines approaching
phenomena. In this paper, we present a typology of the scope of these
potential perspectives. It is not our goal to present a review of terms
and concepts, nor is it to convince all disciplines to adopt a common
suite of terms, which we view as futile. Rather, our goal is to
disambiguate terms, concepts, and processes associated with ‘ensembles’
and ‘ensembling’ in order to facilitate communication, awareness, and
possible adoption of tools across disciplines.