I agree that both of your bullet points would be good. I also think that the second one is extremely non-trivial—more like something it would be good to have a research team working on than something I could write a section on in a blog post.
There’s a sense in which there are already research team equivalents working on it, insofar as lots of forecasting efforts relate to p(crunch time soon). But from my vantage point it doesn’t seem like this community has clarity/consensus around what the best indicators of crunch time soon are, or that there are careful analyses of why we should expect those to be good indicators, and that makes me expect that more work is needed.
I think this can be a useful concept, so thanks for sharing.
I think this post could be usefully expanded on in the following ways:
a bit more detail (vignettes, also, if possible, clear definitions) about what makes a decision important and influencable
what would we have to forecast in order to adjust our credences about whether a crunch time is coming soon
Thanks Sanjay!
I agree that both of your bullet points would be good. I also think that the second one is extremely non-trivial—more like something it would be good to have a research team working on than something I could write a section on in a blog post.
There’s a sense in which there are already research team equivalents working on it, insofar as lots of forecasting efforts relate to p(crunch time soon). But from my vantage point it doesn’t seem like this community has clarity/consensus around what the best indicators of crunch time soon are, or that there are careful analyses of why we should expect those to be good indicators, and that makes me expect that more work is needed.