Multi-assumption modeling (MAM) is an approach to modeling that recognises that multiple-hypotheses or assumptions exist for the way processes work. This is often termed model structural uncertainty, and while recognised, we do not do a good job of fully accounting for this source of uncertainty in model simulations. The goal of MAM and this task within the SFA is to embrace this structural uncertainty by using the multiple hypotheses and assumptions in every modeling activity. Tools to facilitate the MAM approach are becoming available, though they are nowhere near as common as individual models. A major component of this task is the development of the Multi-assumption Architecture and Testbed (MAAT), software designed to easily specify and run large ensembles of simulations. Ensembles can vary in process representation, parameter values, and environmental conditions. Built in sensitivity analysis and uncertainty quantification (SA/UQ) can be used to assess variability in model caused output by multiple ways in which to represent processes. Follow the link above to the MAAT GitHub page for open-source code and more information.