I studied maths, philosophy and genetics at The University of Queensland in Australia. I was drawn to EA through GiveWell and Singerian global health ethics, but am now also interested in animal welfare and the longterm.
I did a biosecurity project at CERI and an AI alignment project at SERI MATS.
Nice! Perhaps when you say Cambridge Centre for Existential Risk you mean the Centre for the Study of Existential Risk? Could you link to their database you mention—I couldn’t find it on a quick search.
Lizka made this nice artwork of me! Insofar as The Elephant in the Brain is right, consider this an especially good social signalling donation opportunity :)
This was nice to read :)
I haven’t thought about this much, but wouldn’t it be theoretically optimal in some sense for people to only vote for one option because there are no increasing marginal costs to subsequent votes to the same candidate? Which is solved by quadratic voting, but I am guessing you considered and rejected it, so quite likely there is something I am missing.
OK makes sense thanks! As I understand GiveWell is interested in/working on incorporating distributions rather than point estimates in general so hopefully discount rates fit in with that work (I don’t have much context on what GiveWell’s plans actually are).
I am a bit surprised you (seemingly) missed my post https://forum.effectivealtruism.org/posts/JvEHiKWWwtvT3YanA/givewell-misuses-discount-rates given the relevance is so clear from the title. I haven’t thought about this more since writing it for the competition, but have no reason to think I was wrong then. The simple idea I propose in that piece is that GiveWell should use a probability distribution over possible discount rates, and that this will meaningfully change the cost-effectiveness of deworming charities.
Excellent, I hope this goes really well!
It would seem better and less confusing to me if the good folk at GWWC (@GraceAdams) add a field in the donation interface with a dropdown menu of which specific CE-incubated charity one wants to donate to (with the default being an agnostic donation for GWWC to distribute between CE charities as they see fit).
Thanks for writing this! I agree making this decision very carefully seems well warranted. One minor thing—you generously offer for people to contact you to ask about out-of-scope things (delivery research). I can well imagine someone finding this post in several years’ time and wanting to contact you, but I’m not sure if this anonymous account will exist/be monitored then; one possible solution is to link to a google form that would go to your email (without revealing what your name/email is).
In your conclusion with the casino analogy, I thought you were going to make an explore-exploit argument, eg:
Once we have played all the different games on offer for a while, we will both get better at each game, and work out which games are most profitable to play. Therefore, we should play lots of games to start with to maximise the information gained.
I think without this argument, the conclusion doesn’t follow. If we are not planning to later narrow down on the best choices once we have learnt more, the case for spreading our resources now seems a lot less strong to me.
Ah good point that we cannot in general swap the order of the expectation operator and an inverse. For scenarios where the cost is fixed, taking the inverse would be fine, but if both the cost and the impact are variable, then yes it becomes harder, and less meaningful I think if the amount of impact could be 0.
Thanks, that was useful and interesting to read, and makes more sense now!
Great you are looking at more direct implementations for increased efficiency, I think my intuition is it would be less hard than you make out, but of course I haven’t seen the codebase so your intuition is more reliable. For the different eras, this would make it a bit harder, but the pmf is piecewise continuous over time, so I think it should still be fine. Keen to see future versions of this! :)
Thanks, that all makes sense, yes I think that is it with the biorisk intervention, that I was only ever seeing a catastrophic event prevented and not an extinction event. For the cost/DALY or DALY/cost, I think making this conversion manually is trivial, so it would makes most sense to me to just report the DALYs/cost and let someone take the inverse themselves if they want the other unit.
I interpreted this in the same way as you. Presumably the referendum will lead to a short-term uptick in popular interest, but maybe our model could be that there is some baseline ‘interestingness’ of an issue that public interest reverts to soon after the referendum? It perhaps depends on what the process for getting something on a referendum is. If it is hard and requires very many people to already endorse the proposed policy, then almost by definition referendums aren’t very neglected. But if a few actors can get a referendum, then the topic may still be quite neglected.
Several (hopefully) minor issues:
I consistently get an error message when I try to set the CI to 50% in the OpenPhil bar (and the URL is crazy long!)
Why do we have probability distributions over values that are themselves probabilities? I feel like this still just boils down to a single probability in the end.
Why do we sometimes use $/DALY and sometimes DALYs/$? It seems unnecessarily confusing. Eg:
“Three days of suffering represented here is the equivalent of three days of such suffering as to render life not worth living.”
OK, but what if life is worse than 0, surely we need a way to represent this as well? My vague memory from the moral weights series was that you assumed valence is symmetric about 0, so perhaps the more sensible unit would be the negative of the value of a fully content life.
“The intervention is assumed to produce between 160 and 3.6K suffering-years per dollar (unweighted) condition on chickens being sentient.” This input seems unhelpfully coarse-grained, as it seems to hide a lot of the interesting steps and doesn’t tell me anything about how these numbers are estimated, and it is not the sort of thing I can intelligently just choose my own numbers for. Also it should be conditional
In the small-scale biorisk project, I never seem to get more than about 1000 DALYs per $1000, even when I crank expansion speed to 0.9c and length of future to 1e8, and the annual extinction risk in era 4 to 1e-8. Why is this? Yes 150,000 is too few, but I thought I should at least see some large effect when I change key parameters by several OOMs. Not really sure what is going on here, I’ll be interested if you replicate this, and whether there is a bug or I am just misunderstanding something.
Thanks for this!
I think it would have been interesting if you had written out some predictions beforehand to compare to the actual lessons. I should have done this myself too perhaps, as in hindsight it is now easy for me to think that it is a priori straightforward that eg the size of the future (time and expansion rate) dominate the EV calculation for x-risk interventions. I think a key value of a model like this could be to compare it to our intuitions/considered judgements to try to work out where they and the model disagree, and how we can change one or the other accordingly.
I am also confused as to why we need/want monte carlo simulations in the first place. My understanding of the model is that cost-effectiveness is essentially the product of several random variables: cost-effectiveness = X * Y * (1/Z) where X~Lognormal(a,b), Y ~ Normal(c,d), Z ~ Beta(e,f). In this case can’t we just analytically compute the exact final probability distribution? I am a bit rusty on the integration required, but in principle it seems quite doable (even if we need to integrate numerically rather than exactly), and like this would give more accurate and perhaps faster results. Am I missing something, why wouldn’t my approach work?
In a separate comment I describe lots of minor quibbles and possible errors.
Record time to safe clinical development
we believe this success is a testament to what’s achievable when a talented, altruistic group of people come together to tackle a seemingly impossible task
Amazing that you got to a clinical trial so quickly! I find myself confused as to how this could happen though: it would be quite surprising to me if other companies and people are just insufficiently clever or hard-working enough or something. Surely there is huge optimisaiton pressure that is already being brought to bear on making R&D and clinical trials go quickly and efficiently as this is a big chunk of how pharma companies make money, and so it seems strange if there were just lots of big wins waiting to be taken in improving the process. What do you think?
I suppose I meant something similar to what Chris has also written. I think being single-minded can be valuable. Hopefully it is possible to engage productively with non x-risk focused communities without being either deceptive or manipulative, I think it is doable, just requires some care I imagine.
How epistemically and optically dangerous do you think the communication and allyship tactics you propose are? Reasons to worry are that:
If we start talking about injustices and near-term non-existential risks that are more ‘sexy’ and easy to grasp, maybe this starts shaping our own thinking as well, which seems bad.
Conversely, if we maintain an x-risk focus while espousing other issues and allying ourselves with other groups, this is (or may be perceived) as a bit deceptive and manipulative.
I think you may well still be right regardless of these risks, but they seem important to consider.
THanks!