 So whereas for linear models piecemeal strategies might be expected to lead to better confirmed models (presuming the target system exhibits only stable linear behavior), no such expectation is justified for nonlinear models deployed in the characterization of nonlinear target systems. Even for a faithful nonlinear model, the smallest changes in either the initial data or the model itself may result in non-proportional changes in model output, an output that is not guaranteed to “move in the right direction” even if the small changes are made “in the right direction” (of course, this lack of guarantee of monotonic improvement also raises questions about what “in the right direction” means, but I will ignore these difficulties here). 