Are all models wrong?

Mathematical modeling in cancer is enjoying a rapid expansion (Brady & Enderling, 2019). For collegial discussion across disciplines, many -if not all of ushave used the aphorism that “All models are wrong, but some are useful” (George Box 1976). This has been a convenient approach to justify and communicate the praxis of modeling. This is to suggest that the usefulness of a model is not measured by the accuracy of representation but how well it supports the generation, testing and refinement of hypotheses. A key insight is not to focus on the model as an outcome, but to consider the modeling process and simulated model predictions as “ways of thinking” about complex nonlinear dynamical systems (Apweiler 2018). Here, we discuss the convoluted interpretation of models being wrong in the arena of predictive modeling.