It's an interesting analysis. Just a thought - since the value of 1 unit is up to the responder if I've understood correctly, it might be more meaningful to calculate ratios of the responses for each person and average these rather than average the responses to each part - for the latter, if any responder picked small "unit" sizes and correspondingly gave large numerical values, they would make an outsized contribution. Calculating ratios first cancels out whatever "unit" people have decided on. Though it should only matter much if people's "units" differ considerably in size.

Thanks for your thoughts and the links. I agree that more consideration of long-term effects and population ethics seems important (also, I would have thought, for the impact of accelerating animal welfare improvements). I don't know anything to go on for quantitative estimates of long-term effects myself, though.

Regarding the possibility of cage-free campaigns as being net negative, I agree this sounds like a risk, so perhaps I was loose in saying donating a certain amount to THL could be "robustly better". I'm not sure it's going to be possible to be 100% sure that any set of interventions won't have a negative impact, though - I was basically going for being able to feel "quite confident" that the impact on farmed animals wouldn't be negative (edit: given the assumptions I've made - all things considered I'm not as confident as that), and haven't been able yet to be precise about what that means. 

Thinking about it, in general, it seems to me that the ranges of possible effects of interventions could be unbounded, so then you'd have to accept some chance of having a negative impact in the corresponding cause areas. Perhaps this is something your general framework could be augmented to take into account e.g. could one set a maximum allowed probability of having a negative effect in one cause area, or would it be sufficient to have a positive expected effect in each area? 

Thanks Michael for the post. I happened to be thinking in similar terms recently regarding how to divide donations between saving human lives and increasing welfare of farmed animals (though nothing like as thoroughly and generally). I thought perhaps this could be an interesting real-world example to analyse:

• This review estimated that saving a life in a very poor country would result in a reduction in births of 0.33-0.5, hence giving 0.5-0.67 extra lives. Though, the uncertainty in the various studies included indicates to me it could plausibly give 1 extra life.
• 1 extra human life perhaps gives ~60 years of extra life (not counting extra descendants, so maybe it's an underestimate).
• Then I remember reading estimates that for every typical westerner there are around 5-10 farmed animals alive at a given time to produce the animals products they eat (though I can't remember the source, and I'm not sure if this includes fish). An extra person in a developing world country isn't going to consume as much as a typical westerner straight away of course, but I suppose the long-term consumption could be this large if they or their descendants reached present western levels of wealth and factory farming was still prevalent then, so it could be a reasonable pessimistic estimate of the effect of saving a human life on the increase in the number of farmed animals.
• This 2018 Founders Pledge report gives a mean estimate of the effect of The Humane League's corporate campaigns alone as "10 hen-years shift from battery cages to aviaries [equivalent] per dollar received" [p.68].
• Perhaps shifting a hen from a battery to aviary system could be taken to be a tenth as good as removing an animal from the system (just based on a not-very-informed intuition).
• So an estimate of an amount to donate to THL to be likely to offset any negative impact on animal welfare from saving a human life (using pessimistic figures to give the rough upper end of the uncertainty range) is [no. of extra human lives] x [no. of extra human years lived per life] x [no. of farmed animals per person]/[no. of animal years saved per $] = 1 x 60 x 10/(10*0.1) = $600.

This is ~20% of the cost of saving one life through Malaria Consortium from  GiveWell's 2020 cost effectiveness analysis. So perhaps this indicates that if you wanted to donate to save lives from malaria but were worried about potential negative impacts on farm animal welfare, splitting donations between MC and THL in a 5:1 ratio would be an option robustly better than doing nothing. (But the THL fraction may need to be higher if impacts on fish, long-term impacts of increasing the human population or other things I've not thought of need to be included).

Does this sound reasonable?

(Edited to correct "4:1 ratio" to "5:1 ratio")

Thanks for this analysis, it's very interesting. You might find it simpler and more accurate to go straight from emissions to warming using the transient climate response to cumulative carbon emissions (TCRE) rather than climate sensitivity, though (see https://en.wikipedia.org/wiki/Transient_climate_response_to_cumulative_carbon_emissions ). A problem with using ECS is that it gives you the warming that occurs after Earth has reached equilibrium with a given CO2 concentration. However, in reality, the CO2 concentration won't stay constant once we've stopped emitting, but will decline as it is slowly taken up by the Earth system. The result found in many Earth system models is that temperatures rise linearly with emissions and once emissions stop, temperatures also stop rising, rather than rising to reach the value implied by the ECS for the peak concentration value (at the point when emissions stop). (Though, temperatures would still rise further if the ECS were very high, since the Earth would be experiencing a much larger radiative forcing in that case.) So I think this would reduce the chance of high temperatures a bit.

Peter here - so actually I'd say this isn't clear now - here's some recent work for example suggesting that estimates of future warming won't change much compared to those from the previous set of models once recent observed warming is used as a constraint i.e. those newer models with higher sensitivity seem to warm too fast compared to observations e.g. https://advances.sciencemag.org/content/6/12/eaaz9549 . Well, the models are only one piece of evidence going into the overall estimate anyway. I don't follow the literature on this closely enough to be confident about what the IPCC will actually conclude.

Very interesting, thanks. I would just suggest adding to your list of caveats at the end that today's rate of warming is much faster than in episodes like the Paleocene-Eocene Thermal Maximum, which increases risks.

I think it would be helpful to establish a norm that people would remove themselves from investigations involving people they have a personal or professional relationship with (which to me means from being on first-name terms upwards or where there is a conflict of interest). Where that is not possible (eg because there would not be enough competent people to do the work) then it ought to be stated what personal or professional relationships exist - but I don't think we need to know whether that relationship is going for the occasional drink or co-hosting weekly orgies...

I also think the form should exist. I would agree that attacks on individuals should be removed (with a comment left explaining why). I'm uneasy about screening the comments more than that, as then people may not trust that no bias has come in. For negative comments about organisations, perhaps people could be encouraged to briefly explain their thoughts and link to evidence. I would hope that people reading the comments would know to take criticism of organisations with no evidence given with a very big pinch of salt, since there will be people around with gripes due to rejected applications etc.

Thanks for this. One thing that perplexes me about the Ricke et al. (2018) analysis is that the SCC for most African countries looks to be lower than for the USA (fig.2), whereas the general consensus seems to be that the impacts of climate change will have far worse effects on individuals' utilities in Africa. So this makes me wonder have they properly captured the effect of marginal utility changing with income? I'm not an economist, so I don't know how to judge this myself.