Yup. Exactly what I thought.

when the Data Generating Model (DGM) is not one of the component models in the ensemble, BMA tends to converge to the model closest to the DGM rather than to the combination closest to the DGM [9]. He also empirically noted that, in the cases he studied, when the DGM is not one of the component models of an ensemble, there usually existed a combination of models that could more closely replicate the behavior of the DMG than could any individual model on their own.

Versus my

if a combination is a better model, either because the true process is a superposition, or we are modelling something outside of our model-space, then a combination will be better able to express it. So mutex assumption will be forced to put all weight on a bad nearby theory,