conformist

Yeah, iterating that a bit further is why paying for political lobbying can be worth it compared to direct donations. However, the less direct your theory of change, the harder to quantify impact and the wider the posterior distribution tends to get. Some of the most impactful things I’ve donated to (imo) have a chance of moving some large scale donations (think Gates) into a marginally more impactful direction.

Value judgment aside: Probably because it works well empirically. If you’re a larger scale donor and you’re interested in maximising impact, you may agree to a matching program type setup to crowd in more donors. Also you feel smart because you crowded in more donors and the donors feel like they got a good deal. So several people feel they can attribute the same counterfactual impact to themselves ;)

Isn’t the point more “it’s easy to fall into the trap to believe that predicting when the sigmoid is going to bend is possible and the right heuristic is to instead extrapolate locally”?

That aside, I’d question whether applying the Lindy effect in particular to something that’s not really a life expectancy but more a growth rate is credible… or perhaps a bit circular since it “assumes away” the ceiling.

Yes it does sound like his approach is quite smart and altruistic, but it’s a shame that that aspect just isn’t covered in more detail.

It is great that he’s doing this and it’s making the world a better place.

It’s a bit disappointing that in articles like this there’s relatively little discussion around what organisations receive the money and what impact it has. We should ultimately judge people by that, not abstractly by “charity == good”? If a billionaire donates millions to the Against Malaria Foundation I would judge that differently than a donation to an art museum in a developed country - and I think people should, and it matters morally.

The difference between for profit and non-profit isn’t really important either compared to “what concretely did they spend money on and what does that plausibly achieve”.

(Tbc some cause areas he donates to are explained, and they seem reasonable and close to his life, but unfortunately not in any depth).

I think what would be great is to have eg a concise example where it works well for you and a concise example where it doesn’t. This shows you have explored it and thought about it enough to explain interesting observations. It’s good to then be ready to go deeper if of interest.

This correlates with tackling the monkey first: https://blog.x.company/tackle-the-monkey-first-90fd6223e04d?...

By this logic a lot of applied maths papers become “does not compile” :D

Yes in theory, but not yet in practice because not everything is fully formalised.

> The authors warn the consequences are already becoming visible. AI-generated papers could overwhelm peer-review systems with low-quality work …

It seems like a key problem here is that peer-review is expected but not explicitly funded/rewarded while it is probably one of the aspects where humans still add a lot of value. Academia’s incentives are hugely misaligned (… as usual unfortunately).

Yeah 3 - 4 is typical in STEM at Imperial, depends on the scholarship or funding source. The standard funding tends to assume 3 - 3.5 years, but I vaguely recall that in some departments supervisors had a habit of forcing people to stick around for a few months without funding.

I was in a similar situation as the parent post and skipped/was moved up by two years of high school.

I think it was very beneficial to have to work hard to catch up with more advanced classes. I feel flexibility around this is something parents and schools should take seriously.

(Tbf I was also super lucky to find a very accepting group of nerdy friends in the new year that would tolerate someone younger.)

There’s also a vague argument around hedging some actual risks that some market participants genuinely want to hedge… which depends a lot on the specific bet. Eg hedging exposure to specific political events, wars or even company announcements can be relevant and worth a premium for non-insiders. Where there’s a premium to be collected there are speculators to do so.

> “Hedge funds invest a ton in "alternative data", like credit card transaction data or satellite-imagery (are Walmart's parking lots full?) and need to process as much relevant information as possible to make predictions that are relevant to investments. “

Ah yes the famous credit card data and Walmart parking lots example that hedge funds were giving a few years ago in every interview and news article. Safe to assume that specifically these data sets are not what you should look at to make money.

By “not terrible” you mean “bad but not very bad” and not “good” right?

It's possible but unlikely given the short timeline, diverse questions that require multiple matheamticians, and low stakes. Also they've already run preliminary tests.

Interesting topic and seems like a reasonable thing to study. It would be cool to see a study where participants behave in a way that leads to more light exposure in month A and the opposite in month B, randomising the time ordering etc.

Despite power analysis and all the “50 people convenience sample, mostly observing based on what people do anyway” seems a bit like it won’t really lead to any action guiding outcomes beyond vaguely confirming people’s priors? As in, perhaps this style of research is not ambitious enough in a way?

Yeah sure SU(2) up to sign is isomorphic to SO(3) and whatnot… I think it’s probably mostly the computer graphics history and the cool name that gets people excited about quaternions?

I like quaternions as much as the next guy (I’ve used them in numerical computations etc), but what is it about them that makes them show up on the front page every few weeks?

As a practitioner in this area, I’d say some of the authors here stand out as extremely influential to this day.

Clearly these include:

Cliff Asness (AQR is huge, lots of publications)

Ronald Kahn (early pioneer, standard book, successful ex. BGI people everywhere)

Neill Chriss (Almgren-Chriss)

Pete Muller (famous stat arb pioneer, PDT still going strong)

Not sure who I’ve overlooked.

Imo the fun quant stuff these days is about predicting returns and not pricing derivatives. As others have said here, the pricing part is mostly commoditised and more about managing software.

Yes the combination of Krylov and quasi-Newton methods are very successful for physics problems (https://en.wikipedia.org/wiki/Quasi-Newton_method).

Iirc eg GMRES is a popular Krylov subspace method.

“Even as quality worsens, prices in drug markets are sticky, so the decreasing potency probably meant people were taking less fentanyl.”

Another essential package is realhats (replace boring \hat with real hats)!

https://github.com/mscroggs/realhats

Yes. Some scientific computing code is still being developed in Fortran eg in HPC. (and has been for decades)

Yeah and this is a much more intuitive way of generalising from the n = 2 case. Weights are proportional to inverse variance even for n > 2. Importantly this assumes independence so it doesn’t translate to portfolio optimisation very easily.

In Germany, cheese with stinging nettle is a thing and I remember eating it a few times as a child (Brennnesselkäse), typically Gouda-like types.

It’s fine to be non-rigorous as long as you’re not a jerk and treat your own conclusions on relatively little data as preliminary. Sometimes it’s good to have some information with relatively large error bars and be willing to update quickly rather than ignore information because the error bars are large. From the article it doesn’t become apparent that the author wouldn’t update her views quickly as new information comes in (eg by talking to someone). So yes the observations in the post are not scientific, but there’s nothing preventing them from being the starting point of rational reasoning and behaviour.

Conditional on “the study being published and getting attention” the real effect is likely smaller and not larger.

Eg if you assume there is a real effect plus a lot of noise, given the study has been published etc the noise will have more likely acted in the favourable direction.

IMHO given the relatively large size of the effect it seems quite likely that the noise part is in fact potentially large (this is much more subjective) which makes is less clear that there is measurable signal at all here. I’d have to see a lot of replication or a very strong explanation of the underlying mechanism to believe the magnitude of the effect, but will very easily believe the sign (with a small magnitude).