Thank you for in-text citation and quality post, felt like quality posts backed by peer-review has been absence from EA Forum lately.

I saw the footnote

Thanks for reading Mike.

With an odd of 1 in 170, that will result in ~$8.964 billion USD (Note that this number and the above numbers are also yearly investment). Not exactly a trillion just yet. The report states that $428.51 trillion over 5-years loss should be considered, if the mean: $85.702 trillion USD GDP loss could be averted by 8.964 billion yearly, this will result in a cost-effectiveness ratio of roughly 9,600:1.

I.e. $1 USD in investment would save ~$9,600 USD in potential economic loss yearly. Although, given my modest understanding of insurance products, if a supereruption does occur,  I suspect that payout to loss will have a P<1 chance of actually materialising. I will need to do more research on this to provide a better answer...

As an overarching review of geospatial data and geoscientists role in EA, I wrote a post and started a channel role-geoscientists on EA anywhere. I would love to see people interested in contributing towards geospatial X-risk here: https://forum.effectivealtruism.org/posts/JaACyD72oacQB9WDn/more-than-earth-warriors-the-diverse-roles-of-geoscientists

Hi Nathan, thanks for reading. The point of this post was really to address 2 problems:

1. It is generally really difficult to find geospatial scientists (Earth Observation, GIS...) and Earth System Scientists (Geophysicist, Volcanologist...) people in EA. We are a minority here. We get really excited when we meet each other. We want to gather a community and say we can contribute.

 2. Geospatial data is very useful in many aspect of our cause areas, hence the breadth of case studies listed above, from neartermist to semi-longtermist (particularly supervolcano impact). While EA as a community don't seem to attract a lot of geoscientists in general (perhaps it should). Current career advice seem to always err on the side of take your quantitative skills and go work in consulting or finance (which was what I did too). We know that geospatial data and our niche skillsets can contribute, therefore, this post is also a chance to inform the rest of EAs the current geospatial research happening. Think about geospatial data and spatial analysis as a "common language" between an epidemiologist and a volcanologist. Perhaps direct cross-pollination might not happen, but a platform for skill-sharing would be useful.

So yes, geospatial research is underrated. I am not arguing more people should become a satellite image analyst, more advocating for if you work in global health or poverty alleviation already, perhaps also pay more attention to the geospatial aspect of it. Additionally, we could be a platform that perhaps provide geospatial research support if this community gain traction.

Hope this clarifies,
Chris

Shouldn't the EMA calculation for 3 years be:

EMA = Current_year*2/(3+1) + Last_yearEMA*(1 - 2/(3+1))

EMA = Current_year*0.5 + Last_yearEMA*0.5

And EMA calculation for 2 years be (your google sheet formula):

EMA = Current_year*2/(2+1) + Last_yearEMA*(1 - 2/(2+1))

EMA = Current_year*0.66 + Last_yearEMA*0.33

However, in the google formula you weighted the previous year by 0.66 and the current year by 0.33, meaning that you gave the most recent data less weightings and the EMA is actually 2 years instead of 3?

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html
https://www.investopedia.com/terms/e/ema.asp
 
A slight change to use a 3 year-smoothing of a 3 year EMA

EMA(3 years) = Fund(t)*0.571+EMA(t-1)*0.286 + EMA(t-2)*0.143

*0.571+0.286+0.143 = ~ 1

I.e. 3 years EMA should actually be:

 

Thanks for reading Larry, 

Your assessment is generally correct, however just one more thing. In ESG ratings, we also take into consideration industry clusters so that companies are ranked against their peers as much as possible. Additionally, in order to have more dimensions, we not only use company self disclosure data but also watch for controversial news, lawsuits.

This means that which data to use and how to normalise or "curve" they grade it on is in the discretion of the industry experts within each ESG rating company.

Of course, at the end of the day, funds hold multiple industries. Which means that they eventually get aggregated so lose some of that granularity.