This is a really good point, and I'm not sure that something exists which was written with that in mind. Daniel Dewey wrote something which was maybe a first step on a short form of this in 2015. A 'concrete-problems' in strategy might be a really useful output from SAIRC.
All the data can be found here.
Often (in EA in particular) the largest cost to a failed started project isn't to you, but is a hard-to-see counterfactual impact.
Imagine I believe that building a synth bio safety field is incredibly important. Without a real background in synth bio, I go about building the field but because I lack context and subtle field knowledge, I screw it up having reached out to almost all the key players. They would now are be conditioned to think that synth bio safety is something that is pursued by naive outsiders who don't understand synth bio. This makes it harder for future efforts to proceed. It makes it harder for them to raise funds. It makes it harder for them to build a team.
The worst case is that you start a project, fail, but don't quit. This can block the space, and stop better projects from entering it.
These can be worked around, but it seems that many of your assumptions are conditional on not having these sorts of large negative counterfactual impacts. While that may work out, it seems overconfident to assume a 0% chance of this, especially if the career capital building steps are actually relevant domain knowledge building.
Yes this is absolutely not a thing that just GPP did - which is why I tried to call out in this post that several other groups were important to recommending it! (And also something I emphasised in the facebook post you link to.)
I don't know how many groups fed into the overall process and I'm sure there were big parts of the process I have no knowledge about. I know of two other quite significant entities that have publicly made very similar recommendations (Angus Deaton and the Centre for Global Development) as well as about half a dozen other entities that made similar but slightly narrower suggestions (many of which we cited). The general development aid sector is clearly enormous, but the field of people proposing this sort of thing is smaller.
Assigning causal credit for policy outcomes is very complicated. It obviously matters to us to assess it, so that we can tell if it's worth doing more work in an area. What we do is just talk to the people we made recommendations to and ask them how significant a role our recommendation played. Usually people prefer we don't share their reflections further, which is unfortunate but inevitable.
At the moment most of the orgs within CEA target 12 months reserves (though some have less and, in particular, they sometimes fall quite low at some point in the course of the year because we avoid on-going fundraising).
If we had something like 3 months of reserves for all costs unrestricted it would give us either greater financial security or the ability to cut the size of restricted overall reserves to, say, 7 months while keeping similar stability. This would free up EA capital for other projects.
It's a little unclear what the right level of reserves ought to be. In the US it's common for charities to have very large endowments (say 20 years). I think the 12 months at all times target we have right now is about appropriate, given the value of capital to EA projects, but would expect that number to drift upwards as the EA community matures.
You're quite right, they are different. At the moment, we are planning to use marginal unrestricted funds to invest in shared services. Partly this aims to increase the autonomy of the shared services function and reduce the extent they feel they need to ask for permission to all the orgs to do useful things.
Past that level though, unrestricted funding would help us build a small reserve of unrestricted money that would provide us with financial stability. Right now, each organisation needs to keep a pretty significant independent runway because virtually all our reserves are restricted. If we had a bigger pool of funds that could go to any org, we could get the same level of financial security with smaller total reserves.
GPP's total budget for 2016 will be roughly £220,000 which is roughly what our minimum target is. The reason there's a discrepancy between this figure and the £95k figure is that the £95k figure presented in the overall CEA budget includes only sums that flow through CEA and doesn't include any shared services. However, GPP is a joint project with FHI, so in 2016 a significant portion of the total costs will be funded via FHI rather than CEA. In addition, we are expecting to hire a seconded civil servant whose salary will be partly funded by the state. This is not counted as part of the CEA budget but is counted as part of the GPP budget.
You can find lots more detail on GPP here
I began my PhD with a focus on Bayesian deep learning with exactly the same reasoning as you. I also share your doubts about the relevance of BDL to long-term safety. I have two clusters of thoughts: some reasons why BDL might be worth pursuing regardless, and alternative approaches.
Considerations about BDL and important safety research:
Big picture, I think intellectual diversity among AGI safety researchers is good, Bayesian inference is important and fundamental, and lots of people glom on to whatever the latest hot thing is (currently LLMs), leading to rapid saturation.
So what is interesting to work on? I'm currently thinking about two main things:
There are other things that are important, and I agree that OOD detection is also important (and I'm working on a conceptual paper on this, rather than a detection method specifically). If you'd like to speak about any of this stuff I'm happy to talk. You can reach me at email@example.com