Assembling ensembling: An adventure in approaches across disciplines

TL;DR

This paper explores the diverse meanings and approaches to model ensembling across disciplines, aiming to clarify terminology and foster cross-disciplinary communication and tool adoption.

Contribution

It presents a typology of ensemble concepts from various fields to disambiguate terms and enhance interdisciplinary understanding.

Findings

Developed a typology of ensemble approaches across disciplines

Clarified terminology related to ensembling and ensembles

Facilitated cross-disciplinary communication on ensemble methods

Abstract

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.