 Mixing, finally, has also been invoked in understanding the effects of structural model error. Frigg, Bradley, Du and Smith (2014) argue that the distinction between parameter error and structural model error is crucial, and that the latter has significant and hitherto unappreciated impact on the predictive ability of a model. Wilson-Mayo (2015) points out that to put this observation on a solid foundation we need a notion of structural chaos. He proposes such a notion by appealing to topological mixing. 