Yes thanks 

boy did this age in favour of "good judgement" as a factor!

To add a small side note to this, in particular the point around the effectiveness essay: 

I suspect the EA community and in particular 80k hours tend to underestimate how hard it is to do better by being more ambitious (for the typical engaged EA, at least). Eg counterfactually increasing your income from 150k to 600k by "being more ambitious" and working longer hours or negotiating your salary more aggressiely is not a very high probability outcome. Achieving this increase by having better judgement around what area to specialise in is perhaps more likely. Likewise, taking more risk by being an entrepreneur does not 10x your career donations in expectation if you have a decent job.
I would discount the multipliers in 6 & 8 a lot (or at least their component attributable to ambition and risk taking), while I believe they are > 1.

Just to point out the obvious: encouraging some of these professionals to think more about earning to give can also be very valuable.

That’s right, but it should be possible to model that in a very similar hierarchical manner and adjust accordingly, too, if you buy into the original framework laid out in the post.

(I haven’t fully thought it thru but it does strike me as fundamentally possible with the same caveats of not knowing parameters, not that I’d suggest using the toy model style maths in practice). 

Thanks for sharing this, awareness of this type of bias is very relevant for the EA community. 

The interpretation of $\sigma_V / \sigma_\mu$ (squared) is subtle in practice. I think a clean way to express it is the (square root of the) ratio of prior precision to “measurement” precision - that fits with the hierarchical model used to explain it in the paper you reference.

In practice this is not trivial to guesstimate. 

An interesting rabbit hole to understand this further is the “Tweedie correction” [1]. 

It should also be pointed out that once you’ve shrunk the estimate, that’s it: EV maximising will pick the posterior winner without accounting for the posterior variance - also something not everyone is comfortable with. 

[1] https://efron.ckirby.su.domains/papers/2011TweediesFormula.pdf

“Trump is pressuring the Fed to adopt policies that would cause inflation.”

That’s more cleanly expressed as a curve steepener (front lower, back higher), so bullish short end vs bearish back. 

“AI-induced job loss might cause the Fed to be less concerned about inflation.”

This sounds more bullish bonds because low inflation concerns -> fed can cut. Also (more importantly) the fed has a dual mandate so low employment -> cut. 

I personally like CTA style trend following strategies but generally advise people who are not very familiar against that exposure. If you think about leverage in percentage terms and not eg in volatility units I would be a bit nervous. 

Levered risk parity (bonds + equities) can get very spicy if correlations break (eg high inflation), but it has worked well in the secular declining rates environment from the 80s to 2010s. 
 

I think Corey Hoffstein’s work and podcast are great!

 

I think it’s a bit tricky to reason about “the ecosystem” on a global level. 
Directionally I’d say earning-to-give deserves more popularity (perhaps even as a default, given direct work seems oversubscribed) and more community support and, yes, it’s hard and can feel less rewarding to find a well paying job and to donate a large fraction of your income!