That's a great question.
I think there are two parts to that question. On the one hand, I think we have to be doing everything we can as quickly as we can because these timelines are really tight. When we're doing our modelling, what's really interesting is that there are some pathways that are quite robust, meaning that as we change our assumptions or different ways in which the future could pan out, our results are quite robust against those.
Then there are other pathways that are less robust, meaning that if we tweak an input assumption one way or another, we get an entirely different pathway or entirely different analysis.
I think that version of sensitivity analysis is really important. Again, this comes back to the modelling work—getting more into the technical weeds here—of developing machine learning models that really help us explore which of those pathways and which of those decisions are robust and which of those decisions are less robust.