Usually, because they help narrow down the sources of error. If you've tested your code so you're sure the various bits work as intended, any remaining somewhat crazy results are down to things you don't understand properly.

A somewhat validated code allows you to do by computational experiments what you can't by real life experiments. Very handy if one of the common end states in the experiments is "death".

The real imperial story is that there were two wrong parties: - The scientists who were not including enough detail in their math. - The drive-by developers who had no idea what did or didn't make scientific simulations work.

Together they produced both an inaccurate estimate, and a github page full of uninformed speculation about what the problem might have been. ;)

Honestly I think the main problem was they choose overly pessimistic estimates, or maybe that their model is so expensive they could only investigate a few choices of the parameters.

same way that a given implementation is better than none. Its a concrete implementation that serves as an anchor. Its a fixed point to discuss implementation issues around as well as working as a harness that others can build on/around.