I think the overall approach you’ve taken is good, and it’s cool to see you’ve worked through this. This is also the kind of example I had in mind, although I didn’t bother to work with estimates.
I do think it would be better to use some projections for animal product consumption and fertility rates in the regions MC works in (I expect consumption per capita to increase and fertility to decrease) to include effects of descendants and changing consumption habits, since these plausibly could end up dominating the effects of MC, or at least on animals (and you also have to decide on your population ethics: does the happiness of the additional descendants contribute to the good compared to if they were never born?). Then, there are also timelines for alternatives proteins (e.g. here), but these are much more speculative to me.
I also personally worry that cage-free campaigns could be net negative in expectation (at least in the short-term, without further improvements), mostly since on-farm mortality rates are higher in cage-free systems. See some context and further discussion here. I believe that corporate campaigns work, though, so I think we could come up with a target for a corporate campaign that we’d expect to be robustly positive for animals. I think work for more humane slaughter is robustly positive. Family planning interventions might be the most promising, see this new charity incubated by Charity Entrepreneurship and their supporting report, including their estimated cost-effectiveness of:
“$144 per unintended birth averted”, and
“377 welfare points gained per dollar spent” for farmed animals. (I don’t know off-hand if they’re including descendants or projected changes in consumption in this figure.)
However, this new charity doesn’t have any track record yet, so it’s in some ways more speculative than GiveWell charities or THL. CE does use success probabilities in their models, but this is a parameter that you might want to do a sensitivity analysis to. (Disclosure: I’m an animal welfare research intern for Charity Entrepreneurship.)
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?
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?
So, it’s worth distinguishing between
quantified uncertainty, or, risk, when you can put a single probability on something, and
unquantified uncertainty, when you can’t decide among multiple probabilities).
If there’s a quantified risk of negative, but your expected value is positive under all of the worldviews you find plausible enough to consider anyway (e.g. for all cause areas), then you’re still okay under the framework I propose in this post. I am effectively suggesting that it’s sufficient to have a positive expected effect in each area (although there may be important considerations that go beyond cause areas).
However, you might have enough cluelessness that you can’t find any portfolio that’s positive in expected value under all plausible worldviews like this. That would suck, but I would normally accept continuing to look for robustly positive expected value portfolios as a good option (whether or not it is robustly positive).
I think the overall approach you’ve taken is good, and it’s cool to see you’ve worked through this. This is also the kind of example I had in mind, although I didn’t bother to work with estimates.
I do think it would be better to use some projections for animal product consumption and fertility rates in the regions MC works in (I expect consumption per capita to increase and fertility to decrease) to include effects of descendants and changing consumption habits, since these plausibly could end up dominating the effects of MC, or at least on animals (and you also have to decide on your population ethics: does the happiness of the additional descendants contribute to the good compared to if they were never born?). Then, there are also timelines for alternatives proteins (e.g. here), but these are much more speculative to me.
I also personally worry that cage-free campaigns could be net negative in expectation (at least in the short-term, without further improvements), mostly since on-farm mortality rates are higher in cage-free systems. See some context and further discussion here. I believe that corporate campaigns work, though, so I think we could come up with a target for a corporate campaign that we’d expect to be robustly positive for animals. I think work for more humane slaughter is robustly positive. Family planning interventions might be the most promising, see this new charity incubated by Charity Entrepreneurship and their supporting report, including their estimated cost-effectiveness of:
“$144 per unintended birth averted”, and
“377 welfare points gained per dollar spent” for farmed animals. (I don’t know off-hand if they’re including descendants or projected changes in consumption in this figure.)
However, this new charity doesn’t have any track record yet, so it’s in some ways more speculative than GiveWell charities or THL. CE does use success probabilities in their models, but this is a parameter that you might want to do a sensitivity analysis to. (Disclosure: I’m an animal welfare research intern for Charity Entrepreneurship.)
Finally, Founders Pledge did a direct comparison between THL and AMF, including sensitivity analysis to moral weights, that might be useful.
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?
So, it’s worth distinguishing between
quantified uncertainty, or, risk, when you can put a single probability on something, and
unquantified uncertainty, when you can’t decide among multiple probabilities).
If there’s a quantified risk of negative, but your expected value is positive under all of the worldviews you find plausible enough to consider anyway (e.g. for all cause areas), then you’re still okay under the framework I propose in this post. I am effectively suggesting that it’s sufficient to have a positive expected effect in each area (although there may be important considerations that go beyond cause areas).
However, you might have enough cluelessness that you can’t find any portfolio that’s positive in expected value under all plausible worldviews like this. That would suck, but I would normally accept continuing to look for robustly positive expected value portfolios as a good option (whether or not it is robustly positive).