Thank you! 

5% does sound very alarming to me, and is definitely a lot higher than I would have said at the beginning of the crisis (without having thought about it much, then). 

Also, beyond the purely personal, are there any actions that could be taken by individuals right now  that would have a positive impact on humanity's chances to recover, conditional on nuclear war? 

Some (probably naive) ideas: 

• Downloading and printing vital information about how to rebuild food supply and other vital infrastructure (so that it can easily be accessed despite varying degrees of infrastructure collapse); I guess ALLFED's articles might be a good starting point (even though I could not quickly find any distilled strategy document/user guide for their research)?
• Increase the likelihood that you will be able to distribute this information: Ensure survival, build local networks, practice leadership skills etc.
• Make sure others do the same: While there already seem to be a lot of preppers, I do not know whether their culture emphasizes strategies for rebuilding a flourishing civilization over mere survival.  "Altruistic prepping" might be a relatively neglected niche (or not, these are just off the cuff thoughts...)

Putin seems to have ordered deterrence forces (which include nuclear arms) to be on high alert, roughly an hour ago. https://www.reuters.com/world/europe/biden-says-russian-attack-ukraine-unfolding-largely-predicted-2022-02-24/

Can someone weigh in about how unprecedented this is? Some media coverage has compared the severity of the current situation to the Cuba Crisis, which would be extremely alarming if remotely true. 

Miscalibration might cut both ways... 

On one hand, It seems quite plausible for forecasts like this to usually be underconfident about the likelihood of the null event, but on the other hand recent events should probably have substantially increased forecasters' entropy for questions around geopolitical events in the next few days and weeks. 

(This risk is a greater risk than the risk of >50% of the public advocating for unethical policies out of self-interest, because in expectation, unethical policies in the self-interest of ">50% of the public" would be good for more people than unethical policies in the self-interest of experts)

This seems to have a bunch of hidden assumptions, including both about the relative capabilities of experts vs. the public to assess the effects of policies, as well as about the distribution of potential policies: While constitutions are not really a technocratic constraint on public opinion, one of their functions appears to be to protect minorities from a majority blatantly using policies to suppress them; in a world where the argument fully went through, this function would not be necessary. 

The fact that 'technocracy' gets named so infrequently by EAs may be a sign that many are advocating for more technocracy without realising it or without realising that the term exists, along with pre-existing criticism of the idea.

While this might certainly be true,  the negative connotations of the term "technocracy" might play an important role here as well: Someone who is aware of the concepts and its criticisms might nevertheless be prompted not to use the term in order to avoid knee-jerk reactions,  similar to how someone arguing for more "populist" positions might not use that term, depending on the audience. 

While I am not sure I agree about the strong language regarding urgent priorities, and would also like to find more neutral terms for both sides, I agree that a better understanding of the balance between expert-driven policy and public opinion would be quite useful; I could imagine that which one is better can strongly depend on specific details of a particular policy problem, and that there might be ways of integrating parts of both sides productively: While I do think that Futarchy is unlikely to work, some form of "voting on values" and relying on expertise for predicting how policies would affect values still appears appealing, especially if experts' incentives can be designed to clearly favor prediction accuracy, while avoiding issues with self-fulfilling prophecies. 

Do you have thoughts on how potentially rising inflation could affect emission pathways and the relative cost of renweables? I have heard the argument that associated rises in the cost of capital could be pretty bad, because most costs associated with renewables are capital costs, while fuel costs dominate for fossil energy. 

Huh? I did not like the double-page style for the non-mobile pdf, as it required some manual rescaling on my PC.

And the mobile version has the main table cut between two pages in a pretty horrible way. I think I would have much preferred a single pdf in the mobile/single page style that is actually optimized for that style, rather than this.

Maybe I should have used the HTML version instead?

More detailed action points on safety from page 32: 

The Office for AI will coordinate cross-government processes to accurately assess long term AI safety and risks, which will include activities such as evaluating technical expertise in government and the value of research infrastructure. Given the speed at which AI developments are impacting our world, it is also critical that the government takes a more precise and timely approach to monitoring progress on AI, and the government will work to do so. 

The government will support the safe and ethical development of these technologies as well as using powers through the National Security & Investment Act to mitigate risks  arising from a small number of potentially concerning actors. At a strategic level, the National Resilience Strategy will review our approach to emerging technologies; the Ministry of Defence will set out the details of the approaches by which Defence AI is developed and used; the National AI R&I Programme’s emphasis on AI theory will support safety; and central government will work with the national security apparatus to consider narrow and more general AI as a top-level security issue.

I don't think I get your argument for why the approximation should not depend on the downstream task. Could you elaborate? 

I am also a bit confused about the relationship between spread and resiliency: a larger spread of forecasts does not seem to necessarily imply weaker evidence: It seems like for a relatively rare event about which some forecasters could acquire insider information, a large spread might give you stronger evidence. 

Imagine $E$ is about the future enactment of a quite unusual government policy, and one of your forecasters is a high ranking government official. Then, if all of your forecasters are relatively well calibrated and have sufficient incentive to report their true beliefs,  a 90% forecast for $E$ by the government official and a 1% forecast by everyone else should likely shift your beliefs a lot more towards $E$ than a 10% forecast by everyone.   

 

This seems to connect to the concept of $f$- means: If the utility for an option is proportional to $f\left(p\right)$, then the expected utility of your mixture model is equal to the expected utility using the $f$-mean of the expert's probabilities $p_1$ and $p_2$ defined as $f^{-1}\left(\frac{f\left(p_1\right)+f\left(p_2\right)}{2}\right)$, as the $f$ in the utility calculation cancels out the $f^{-1}$.  If I recall correctly, all aggregation functions that fulfill some technical conditions on a generalized mean can be written as a $f$-mean.  

In the first example, $f$  is just linear, such that the $f$-mean is the arithmetic mean. In the second example,  $f$ is equal to the expected lifespan of $\frac{1}{1-(1-p)}=\frac{1}{p}$ which yields the harmonic mean. As such, the geometric mean would correspond to the mixture model if and only if utility was logarithmic in $p$, as  the geometric mean is the $f$-mean corresponding to the logarithm.  

For a binary event with "true" probability $q$, the expected log-score for a forecast of $p$ is $q\log \left(p\right)\ast (1-q)\log (1-p)=\log \left(\frac{p^q}{(1-p{)}^{1-q}}\right)$, which equals $\log \left(\sqrt{\frac{p}{1-p}}\right)=0.5\log \left(\frac{p}{1-p}\right)$ for  $q=0.5$. So the geometric mean of odds would optimize yield the correct utility for the log-score according to the mixture model, if all the events we forecast were essentially coin tosses (which seems like a less satisfying synthesis than I hoped for).

Further questions that might be interesting to analyze from this point of view:

• Is there some kind of approximate connection between the Brier score and the geometric mean of odds that could explain the empirical performance of the geometric mean on the Brier score? (There might very well not be anything, as the mixture model might not be the best way to think about aggregation).
• What  optimization target (under the mixture model) does extremization correspond to? Edit: As extremization is applied after the aggregation, it cannot be interpreted  in terms of mixture models (if all forecasters give the same prediction, any $f$-mean has to have that value, but extremization yields a more extreme prediction.)

Note: After writing this, I noticed that UnexpectedValue's comment on the top-level post essentially points to the same concept. I decided to still post this, as it seems more accessible than their technical paper while (probably) capturing the key insight.

Edit: Replaced "optimize" by "yield the correct utility for" in the third paragraph.