Thanks, that's very useful for me trying to follow but not that deep in the models!

I feel like the overall takeaway is very different though. I've not fully understood the details in either argument so this is a little vibes based. You seemed to be arguing that below subsistence wages were fairly likely while here it seems to be that even falling wages require a weird combination of conditions.

What have I misunderstood?

This seems like good work but the headline and opening paragraph aren't supported when you've shown it might be log-normal. Log-normal and power distributions often have quite different consequences for how important it is to move to very extreme percentiles, and hence this difference can matter for lots of decisions relevant to EA.

While Sam Bowman is interesting and seems ideologically similar to many EAs, it's not obvious to me why he's on the 80k podcast. Does 80k think UK housebuilding is one of the world's most pressing problems? Is there something else I'm missing?

Naively, this seems like a great fit for EAIF, which is looking to fund more projects. Is there something I'm missing?

Type of estimate: Geometric mean of forecaster probabilities

This is a bit odd. Should probabilities be odds?

Could you please expand on why you think a Pareto distribution is appropriate here? Tail probabilities are often quite sensitive to the assumptions here, and it can be tricky to determine if something is truly power-law distributed.

When I looked at the same dataset, albeit processing the data quite differently, I found that a truncated or cutoff power-law appeared to be a good fit. This gives a much lower value for extreme probabilities using the best-fit parameters. In particular, there were too few of the most severe pandemics in the dataset (COVID-19 and 1918 influenza) otherwise; this issue is visible in fig 1 of Marani et al. Could you please add the data to your tail distribution plot to assess how good a fit it is?

A final note, I think you're calculating the probability of extinction in a single year but the worst pandemics historically have lasted multiple years. The total death toll from the pandemic is perhaps the quantity most of interest.

If you were willing to start putting doses in arms without any safety or efficacy testing at all, that's when you could start.

I think even this is pretty optimistic because there was very little manufacturing capacity at that point.

I don't see how you can say both that it will "almost never" be the case that NYC will "hit 1% cumulative incidence after global 1% cumulative incidence" but also that it would surprise you if you can get to where your monitored cities lead global prevalence?

Sorry, this is poorly phrased by me. I meant that it would surprise me if there's much benefit from adding a few additional cities.

The best stuff looking at global-scale analysis of epidemics is probably by GLEAM. I doubt full agent-based modelling at small-scales is giving you much but massively complicating the model.