[rcthompson]

There is absolutely a reason to run a model under such unreasonable assumptions. It gives an upper bound. If you ran that unreasonable model and found that the cancer risk was minimal, then you could say with confidence that under a more reasonable model that doesn't massively overestimate the level of exposure, the risk would not be any higher and would likely be a lot lower. As others have noted though, that's not the result that this model produced, so another more realistic model should have been run.

[MostlyStable]

Having an upper bound is super useful...when paired with more reasonable estimates. But if you are only going to run _one_ set of numbers (which appears to be the case here), it's much _less_ useful than having just the "reasonable" numbers.

[rcthompson]

The point is that the upper bound model is much, much easier to run, since you don't have to do any complicated modeling of how much of the substance people are actually exposed to, you just assume the worst case. If this model already gives you the right answer, it saves you the considerable work of coming up with a realistic exposure estimate (and then the additional work of explaining this estimate in a way that people believe you, etc.).

So it's not a case of running 1 model vs. running 2 models, it's running 1 dead simple model vs. 1 much more difficult and complicated model. If the simple model is good enough, you just want to run that. In this case it wasn't, though.