simon

Karma: 85

simon 12 Jun 2026 14:10 UTC
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in reply to: Noah Birnbaum’s comment on: Noah Birnbaum’s Quick takes
An important (and to me fairly open) related question is to what extent this ends up being action-guiding?
Eg if I lower my probability estimate of this materialising by 10 percentage points, how much will it affect resources I spend on helping to prepare for the possible outcome? Perhaps people here have thoughts on that—my impression is that working on improving the future opportunity set for such donations is relatively robustly good right now?

simon 12 Jun 2026 13:58 UTC
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in reply to: James Greathouse’s comment on: When the downside is limited and the upside is not, variance is your friend. When the downside is possibly very negative, it is not.
Yes, I agree, it is non-trivial to assess (approximately, probably) bounded downside and this needs to be done on a case-by-case basis, including in global health.
However, if you find that there are fat tails on a portfolio level but none on the individual intervention level, then the individual intervention was probably not assessed comprehensively? Interaction effects and second order effects matter, of course, but at least a portion of them should arguably be credited back to individual iterventions as you assess them.

simon 6 Jun 2026 19:12 UTC
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on: Global Health Charity Founders on GiveWell, Evidence Action, and M+E
This is a super valuable article, thank you for sharing!

Based on this, would it be sensible or interesting to fund a third-party M&E organisation, whose costs are considered somewhat separately, that provides independent evaluation at a relatively early stage and that builds more knowledge around how to do this well over time? Are there funders that might be interested in this?

The exchange rate & inflation fluctuation argument is also interesting. Clearly, EA organisations won’t plausibly be able to predict this (otherwise they should be trading currencies!).
However, the volatility can be modelled reasonably well, and this is a source of uncertainty in CE estimates that should be considered. However, it is usually not communicated. RCTs’ uncertainty, implementation biases and publication bias are somewhat similar in nature. They are tricky to model, but even a rough model would be great.

I would love to see more CE estimates whose output is an entire distribution and not just a point estimate (looking at you, GiveWell ;) ). This can be done, even very approximately, and it would be an improvement over what is commonly shared. I feel a reasonable version of this could be vibecoded fairly quickly?

simon 4 Jun 2026 21:54 UTC
2 points
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on: EA 1.0 vs. EA 2.0
There does indeed seem to be a split in the community, but I’m not sure it’s great to work towards that rather than against it.
I kind of try to speak to folks in EA 2.0 occasionally despite being pretty squarely in EA 1.0 and that’s probably net positive, e.g. to avoid a complete echo chamber?

simon 29 Apr 2026 18:45 UTC
2 points
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in reply to: david_reinstein’s comment on: Forecasting is Way Overrated, and We Should Stop Funding It
Yes, funding eg “research that also produces forecasts” seems in a completely different category to me than eg prediction markets or platform building.

I feel the original article perhaps conflates different types of “forecast” funding a bit too much, although I tend to agree with its overall sentiment.

simon 26 Apr 2026 17:09 UTC
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in reply to: Elliott Thornley (EJT)’s comment on: AI safety can be a Pascal’s mugging even if p(doom) is high
Yes, exactly. My point is that people are pretty aware and claiming otherwise is a bit of a straw man type fallacy—but I might be wrong, perhaps I interact with different people :D

simon 26 Apr 2026 11:00 UTC
1 point
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on: AI safety can be a Pascal’s mugging even if p(doom) is high
I think this is essentially a straw man?
Everyone I know who doesn’t like donating to AI safety basically thinks it’s because p(influencing the outcome positively) is too low.

simon 26 Apr 2026 10:11 UTC
4 points
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on: Forecasting is Way Overrated, and We Should Stop Funding It
Prediction markets seem to be a great business (mostly gambling with all the problems associated with it) so “funding” in the sense of investing in them could be sensible while “funding” in the donation sense not. (And then later donation to AMF or similar).

In general, I’m hesitant to donate to stuff that’s plausibly just a really good business in its own right.

simon 24 Feb 2026 11:57 UTC
1 point
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in reply to: Linch’s comment on: Linch’s Shortform
Note that in the context of trading/investing, the two terms are often used differently. There, “mean reversion” often means negative autocorrelation of returns, which can either be ~causal or driven by price level noise (which in turn is more like a “regression to the mean” idea). If you invest in a mean reversion strategy you tend to have an actual mechanism in mind though.

“Regression to the mean” is a less ambiguous term and generally means what you describe.

simon 24 Feb 2026 11:02 UTC
1 point
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on: I wrote a book about philanthropy: Twice As Good!
Thanks a lot Joey, this is definitely worth reading for people in the wider EA space, not only larger scale donors or people working in philanthropy directly.

What I’ve found particularly helpful are the rough quantitative guidelines regarding “charity time consumed per amount donated” and “how to donate as a function of annual amount and time spent per week”.

This is very valuable to better position myself from an earning-to-give perspective.

I think it might perhaps be interesting to write a short summary of that for the forum, perhaps targeted more at a median e2g EA? (If that doesn’t exist already.)
Separately, it’s great to see that the book really embraces plurality in what areas donors prioritise without too strong a view on what’s preferable in the author’s opinion.

simon 18 Feb 2026 18:50 UTC
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in reply to: Alex319’s comment on: Our Levels of Ambition Should Match The Problems We’re Solving
Yes thanks

simon 15 Feb 2026 18:41 UTC
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in reply to: Matt Putz’s comment on: Effectiveness is a Conjunction of Multipliers
boy did this age in favour of “good judgement” as a factor!

simon 15 Feb 2026 18:25 UTC
3 points
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in reply to: Mo Putera’s comment on: Our Levels of Ambition Should Match The Problems We’re Solving
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.

simon 12 Feb 2026 21:09 UTC
10 points
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on: 500k mid-career professionals want to do more good with their careers. Can we help them?
Just to point out the obvious: encouraging some of these professionals to think more about earning to give can also be very valuable.

simon 12 Feb 2026 20:42 UTC
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in reply to: NickLaing’s comment on: The best cause will disappoint you: An intro to the optimisers curse
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).

simon 11 Feb 2026 23:35 UTC
4 points
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on: The best cause will disappoint you: An intro to the optimisers curse
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

simon 21 Jan 2026 0:36 UTC
1 point
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in reply to: PeterMcCluskey’s comment on: Return Stacked Funds: A New Way to Get Leverage
“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.

simon 21 Jan 2026 0:31 UTC
1 point
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in reply to: PeterMcCluskey’s comment on: Return Stacked Funds: A New Way to Get Leverage
“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.

simon 21 Jan 2026 0:17 UTC
3 points
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on: Return Stacked Funds: A New Way to Get Leverage
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!

simon 20 Jan 2026 23:46 UTC
1 point
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in reply to: 10jwahl’s comment on: Is there a structural mismatch between EA-aligned talent and opportunities?
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!