You are making some good points here. It is expensive and exacting work to, for instance, set up validation experiments, or to quantify errors exactly enough s

You are making some good points here. It is expensive and exacting work to, for instance, set up validation experiments, or to quantify errors exactly enough so that whatever errors that do appear are explained by known effects. Agencies don’t like to pay for this. One forcing function can be if high-stakes decisions rest on the conclusions from the code. In the geosciences, you start to see some of this care for air quality measurements and for climate science. (One relevant journal here is SIAM J. UQ., https://www.siam.org/Publications/Journals/SIAM-ASA-Journal-on-Uncertainty-Quantification-JUQ https://www.siam.org/Publications/Journals/SIAM-ASA-Journal-...) The lab where I work has spent a lot of resources on validating thermal and fluid dynamical codes for atmospheric entry of Mars landers — an engineering example rather than science. They obviously are very motivated to avoid an unpleasant surprise from a model failure.