 For nonlinear models, faithfulness can fail and piecemeal perfectibility cannot be guaranteed, raising questions about scientific modeling practices and our understanding of them. However, the implications of the loss of linear superposition reach father than this. Policy assessment often utilizes model forecasts and if the models and systems lying at the core of policy deliberations are nonlinear, then policy assessment will be affected by the same lack of guarantee as model confirmation. Suppose administrators are using a nonlinear model in the formulation of economic policies designed to keep GDP ever increasing while minimizing unemployment (among achieving other socio-economic goals). While it is true that there will be some uncertainty generated by running the model several times over slightly different data sets, assume that policies taking these uncertainties into account to some degree can be fashioned. Once in place, the policies need assessment regarding their effectiveness and potential adverse effects, but such assessment will not involve merely looking at monthly or quarterly reports on GDP and employment data to see if targets are being met. The nonlinear economic model driving the policy decisions will need to be rerun to check if trends are indeed moving “in the right direction” with respect to the earlier forecasts. But, of course, data for the model now has changed and there is no guarantee that the model will produce a forecast with this new data that fits well with the old forecasts used to craft the original policies. Nor is there a guarantee of any fit between the new runs of the nonlinear model and the economic data being gathered as part of ongoing monitoring of the economic policies. How, then, are policy makers to make reliable assessments of policies? The same problem—that small changes in data or model in nonlinear contexts are not guaranteed to yield proportionate model outputs or monotonically improved model performance—also plagues policy assessment using nonlinear models. Such problems remain largely unexplored. 