I agree with your point etc.
Just on a purely philosophical level: it could also feel less unfair in expectation, if you randomise and state the rules and your lack of conviction in ranking etc up front in the sense that it can net reduce biases and people get exactly what they agree too.
You could even issue a certificate saying “was in the selected cohort, but didn’t win the lottery” or similar to reduce some of the signalling effect.
Yeah I guess it’s ethically questionable if you’re certain about the rank ordering.
My guess is that the relative ranking between the 2n best applicants is largely noise in any case, if you’ve got many good applicants (it’s hard to measure and predict skills etc). So as long as you make this clear up front, having a lottery component in a process that’s very noisy should be ethically fine?
Why don’t funders require randomisation? Eg pick the 2n best applications, randomly admit the n best, track outcomes etc.
Im sympathetic to the argument, and I’d go so far as to say: Predictability is so low, the EV of most possible actions is zero (partially due to symmetry, I think cluelessness gets this ~ wrong) unless I manage to find a small pocket of predictability of how my actions meaningfully impact the world.
So I’m largely very short-termist in what I do. Is this different to what you’re saying here?
How does diversification follow?
Intuitively this argument is made a lot, but the precise mechanism matters?
Yes this was clear—imho you underestimate how sophisticated investors in hedge funds (what you call “clients”) are. The best hedge funds charge way more than 2% and it can still be a rational investment. “Value to society” is not a criterion that matters. “Value to investors” is.
There are many mutual funds that charge over 1%, add little value and target unsophisticated investors. That’s the case where I buy your point.
Have you thought about including an automatic inflation adjustment?
Yeah I think the obvious Bayesian reply is: Just make your prior less informative if you know less? It’s fine to choose it to be close to uniform over a very wide range if you know close to nothing.
I think the main concern is that people often use the expected value of such a wide distribution in utility maximisation. So the concerning part is the interaction between eg utilitarianism and Bayesian inference.
People in EA should definitely read more Feyerabend! (Or ask llms what Feyerabend would say about a topic etc).
For example “a complete theory of scientific epistemology” is something he’d most likely reject even as an ideal.
Bayesian inference and decision making are somewhat distinct steps.
EV maxxing is a specific (and specifically simple) objective function that you can plug Bayesian estimates into for decision making. It removes the need to think about distributions.
I think it can be helpful to separate out the problems—eg I believe that there aren’t really any plausible alternatives to Bayesian inference (over future trajectories of the world as a function of your actions or similar) but I think there’s much more room for debate regarding the objective function.
The hedge fund industry is not based on this. Most hedge fund investors are sophisticated and they often pay more than 2%.
I see, fair.
What’s the alternative to “wide distribution stays wide?” in practice?
Separately, “very wrong and narrow prior” is a problem of course, but very much intra-Bayesian and not a criticism of Bayesian reasoning?
Nice distinction!
I’d agree that pop-Bayesianism is overused while more rigorous Bayesianism is underused.
Can you elaborate? Do you mean “should” in the sense that it possibly ought not to do that or do you mean it might not be close enough? (In the latter case it seems to be more about specific parameters in practice rather than the concept itself?)
Reason 2 (edit: now 3) is not a valid criticism of correctly applied Bayesian thinking. If your initial guesses are arbitrary and weak and you are aware, you have a very wide prior. So updating will bring your posterior arbitrarily close to the right answer.
Of course it’s reasonable to criticise incorrectly applied Bayesian thinking or overconfidence in your prior, or too weak updating, or anchoring.
Yeah this has indeed never really been the definition of a hedge fund. Only a subset of hedge funds are approximately point-in-time market neutral.
It seems prudent that banks were quick to margin call him, they probably tightened this kind of stuff after archegos. They still had net assets, so this was early enough.
Their gains were from leveraged directional bets and their losses were from leveraged directional bets. Seems they were margin called, so their liquidity was squeezed—poor risk management. It seems that they have learned from this and are now using “fully paid for” derivatives, so presumably call options whose premium they can afford.
Hedge funds are not necessarily hedged, but eg long/short equity hedge funds are (while macro hedge funds can have all kinds of unhedged macro bets on.)
Sadly it appears that adjacency to EA correlates with poor risk management, I’m wondering whether this is somewhat structural (beyond eg the age group…)
These podcasts are such a great format to learn about GiveWells work and to gain confidence in the process!
To single out one specific point … The sentiment around feeling indebted to FTX and by proxy people associated with it seems like a deeply worrying and flawed viewpoint?
If the argument was purely “she’s smart and can likely do good work, let’s give her a chance”, I’d be far less concerned.