One of the main motivations for designing computer models of complex systems is to come up with recommendations on how to best control these systems. Many complex real-life systems are so complicated that it is not computationally possible to use realistic nonlinear models to find the corresponding optimal control. Instead, researchers make recommendations based on simplified -- e.g., linearized -- models. The recommendations based on these simplified models are often not realistic but, interestingly, they can be made more realistic if we "tone them down" -- i.e., consider predictions and recommendations which are close to the current status quo state. In this paper, we analyze this situation from the viewpoint of general system analysis. This analysis explain the above empirical phenomenon -- namely, we show that this "status quo bias" indeed helps decision makers to take nonlinearity into account.