Oscar Delaney

392Brisbane QLD, AustraliaJoined Apr 2021



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.


Hi Sela, thanks for the long and thoughtful comment, and for your kind words. That is reassuring that you also do not feel this is a key area for GiveWell/OP to expand into.

Really interesting re EMDAT possibly being off by ~10x, I was aware that longer-term harms are a lot harder to measure but wasn't expecting the effect to be that large.

Re my references to ending all flooding harms, that makes sense; I wasn't trying to suggest that the average cost-effectiveness would be the same as marginal cost-effectiveness. Perhaps a better thing to say would be that in order to be competitive with top charities, marginal targeted interventions would need to be far better than the average of existing interventions.

Hmm yes I was a bit surprised at how expensive EWS were made out to be, particularly when I would have thought a lot of costs could be saved by rolling out the same model and infrastructure across different countries.

Thanks for the offer, I am not currently working on this and don't expect to go back to it, so I don't think there would be much value in talking further - I'll let you know if I am coming back to this though. I hope you make great progress on your flood forecasting work!

OK nice, thanks for the prompt changes, especially the new income effects part of the model!

Hi Francis, thanks for this thoughtful write-up, I didn't realise so many people were exposed to dangerous levels of arsenic!

I have some (perhaps) sad news though, that I believe your cost-effectiveness estimates are 10x too optimistic: you list the annual mortality rate in Bangladesh as 5.5%, however the source you cite gives the (far more plausible - 5% would imply a life-expectancy on the order of 20) figure of 5.5 per thousand.

This, combined with the fact that I basically buy your/others' critique of Argos 2010 that not taking into account socioeconomic differences makes the correlational analysis not very informative, makes me think the bottom line is arsenic interventions are very likely not on the same order of cost-effectiveness as GiveWell top charities.

Given the above, perhaps this is a moot point, but I am interested in adsorption because you say it is the best intervention. I think it would be useful to say something about the practicalities of the process. Is it as simple as dropping a bag of chemicals into a well and letting chemistry happen? It feels important to me to know how simple (and hence scalable) the intervention is.

Finally, two suggestions on communication:

  • I think the interventions should be listed in the same order in-text and in the table (and it would be an added bonus if interventions were headings and so showed up in the outline on the left of the forum post).
  • Your spreadsheet has hard-coded number, instead, for instance I2 should be "=E2*H2" not "=0.003*0.055". Otherwise when you fix the mortality numbers nothing will happen in the downstream cells.

Thanks for this! I liked the typology of biosurveillance systems. I was confused though by the 'Human / Animal Monitoring' category: why are there humans in this category? If we are testing humans exposed to farm animals, I thought this would fit in the 'sentinel' category. It would make more sense to me if this category was just for testing animals.

I appreciate your willingness to change your mind and make this difficult decision. I think this is a big part of what makes EA great, thank you. (I make no comment on whether this is the correct decision at the object level, I don't really know enough to say.)

This analysis seems fair to me. One mitigating feature I think is that precisely because the vacuous applause light statements aren't meant to be action-guiding, they generally are not action-guiding. Hopefully Bob makes his remark and then people nod wisely and go on with selecting the best candidates as if ~nothing had happened. I think there is still a danger that something that should have been a mere applause light is misinterpreted as action-guiding (as Alice did here) but this time by Chloe who accepts the remark uncritically. Chloe may then go on to form real policies and plans based on Bob's remark. So yes this seems bad, but hopefully not really bad. In terms of what to do about it, perhaps if more people start engaging critically with applause light statements and they are shown up to be without much thought or substance, this renders making such statements negative in terms of social value and fixes the problem. But perhaps more likely is that the people engaging critically with applause lights are hounded down for being insensitive. Tricky.

Thanks, I hadn't read that post, very interesting! I did read in one of the review papers on flooding that there can be injuries from being confined with animals, but chose not to included it as I didn't see any data on it and my subjective impression was that this would not be as large an impact as the others (but this could be wrong, if anyone finds good info on the flood --> bites causal link I'd be keen to see it).

This 100x change is huge, and I think deserves a prominent note in the main text, either significantly rewriting the cost-effetiveness section based on this number, or at least putting something like [EDIT: this cost-effectiveness calculation is no longer endorsed by the author, due to a mistake in the paper cited]. That said, I think this is not as irreconcilable as it seems, as the new 149k DALYs figure is only referring to Punjab, Haryana and Delhi, which account for ~5% of India's population, whereas the 66k deaths figure is for India as a whole. It is not as simple as just multiplying through by ~20 to get the DALYs for the whole country, as the impression i get is that those states are unusually bad for crop residue burning. Great post though, I would be excited to see more work on this :)

FYI, I added an edit at the end of the summary reflecting some of the discussion in the comments.

Hmm yes policy work is tricky - probably even harder to model in a CEA than the more physical interventions I was mainly thinking about. I suppose this is what I was gesturing at with "So plausibly EAs that have significant sway over government decision-makers and can convince them to invest more in flood defences should do this. This would be the case for almost any good policy though: if it is low-effort to convince the government to do it, you should." But yes perhaps I did undersell the value of policy here. I think I mostly stand by my claim that if you are able to influence policy a lot you should probably focus on other things first. If as you say the policies needed for flooding are unusually tractable then yes that would change things.

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