I like the claims and the epistemic bridge, great post.
The “global wheat and barley export reduction” AFAIK was closer to 4% and inflated because of some statistical misrepresentations in the media. Price hikes are accurate though and that only adds to your argument. 4% reduction is big in a system that needs robustness of 97%+.[1]
As a tractable problem, there’s more criticism, i.e. it’s hard to see exactly what I could do except a drought monitoring system. 5% of global climate investments is still much more than EA funding combined. Other potential solutions seem to be about assisting governments (which governments do pretty well) and creating new infrastructure in underserved areas. However, if we do it at the wrong spot, that long-tail catastrophe will not be alleviated by our solution. However, there are more tractable and neglected ideas in this vein, e.g. quick brainstorm:
Increasing global coordination and “friendship”, i.e. a political mediation cause area
Reducing centralization of core infrastructural planning, e.g. ensuring national independence from EU (see Switzerland’s intranational governance design)
Creating more institutions like Our World in Data that can inference, monitor, and assist in situations like Covid-19, climate change, drought prediction, etc. (which they are doing an excellent job of with only 25 people)
Creating complex systems models of critical infrastructure in the world. The OECD, UN, WHO already does a bit of this but they’re not very good. Creating more and better of these might assist in predicting long-tail risks.
And the reason for linear effects focus is that nonlinear dynamical systems are rapt with long-tail, low-probability, catastrophic events which are hard to predict, i.e. nth order effects end up being hard to act on compared to linear effect, hence GiveWell et al. I still think it’s highly relevant to include in our work but it’s definitely harder to act on.
I like the epistemic bridge—I agree that longtermism is too segregated from the existing ML networks and not embedded in society for reasons that MichelJusten also mentions.
I like the claims and the epistemic bridge, great post.
The “global wheat and barley export reduction” AFAIK was closer to 4% and inflated because of some statistical misrepresentations in the media. Price hikes are accurate though and that only adds to your argument. 4% reduction is big in a system that needs robustness of 97%+.[1]
As a tractable problem, there’s more criticism, i.e. it’s hard to see exactly what I could do except a drought monitoring system. 5% of global climate investments is still much more than EA funding combined. Other potential solutions seem to be about assisting governments (which governments do pretty well) and creating new infrastructure in underserved areas. However, if we do it at the wrong spot, that long-tail catastrophe will not be alleviated by our solution. However, there are more tractable and neglected ideas in this vein, e.g. quick brainstorm:
Increasing global coordination and “friendship”, i.e. a political mediation cause area
Reducing centralization of core infrastructural planning, e.g. ensuring national independence from EU (see Switzerland’s intranational governance design)
Creating more institutions like Our World in Data that can inference, monitor, and assist in situations like Covid-19, climate change, drought prediction, etc. (which they are doing an excellent job of with only 25 people)
Creating complex systems models of critical infrastructure in the world. The OECD, UN, WHO already does a bit of this but they’re not very good. Creating more and better of these might assist in predicting long-tail risks.
And the reason for linear effects focus is that nonlinear dynamical systems are rapt with long-tail, low-probability, catastrophic events which are hard to predict, i.e. nth order effects end up being hard to act on compared to linear effect, hence GiveWell et al. I still think it’s highly relevant to include in our work but it’s definitely harder to act on.
I like the epistemic bridge—I agree that longtermism is too segregated from the existing ML networks and not embedded in society for reasons that MichelJusten also mentions.
It might be different from 4% but it is lower than a third of global exports, depending on what “disrupted” means.