Michael notes that the question of “How promising is a given cause area X rather than a cause area Y?” is actually “How cost-effective are the best available interventions for addressing X rather than Y?”.
Michael Plant made a similar point in the comment you cite at the start of this post. I responded that I didn’t think the point was quite right. The fleshed out picture of Michael’s reasoning given in this post resolves some of what I said in response (regarding not knowing in advance what the best interventions in each area are). But I think the reasoning given in this post still isn’t quite right, because I don’t think we only care about the best interventions in each area; I think we also care about other identifiable positive outliers. Reasons for that include the facts that:
We may be able to allocate enough resources to an area that the best would no longer be the best on the margin (if there are diminishing returns)
Some people may be sufficiently better fits for something else that that’s the best thing for them to do
And there are probably cases in which we have to or should “invest” in a cause area in a general way, not just invest in one specific intervention. So it’s useful to know which cause area will be able to best use a large chunk of a certain type of resources, not just which cause area contains the one intervention that is most cost-effective given generic resources on the current margin.
For example, let’s suppose for the sake of discussion that technical AI safety research is the best solution within the x-risk cause area, that deworming is the best solution in the global health & development cause area, and that technical AI safety is better than deworming. (Personally, I believe the third claim, and am more agnostic about the other two, but this is just an example.) In that case, in comparing the cause areas (to inform decisions like what skills EAs should skill up in, what networks we should build, what careers people should pursue, and where money should go), it would still be useful to know what the other frontrunner solutions are, and how they compare across cause areas.
I think this is, to some extent, a reason why GiveWell’s definition (mentioned at the end of this post) is useful.
Hmm. It seems like the only way this differs from my account is that ‘cause comparisons’ are/should be the comparison of the top interventions, rather than just intervention. But the ‘cause comparison’ is still impossible without (implicitly) evaluating the specific things you can do.
Yes, I think that sounds correct to me. I think that that’s what I was trying to get across with “But I think the reasoning given in this post still isn’t quite right, becauseI don’t think we only care about the best interventions in each area; I think we also care about other identifiable positive outliers.”
I.e., I do think that, other that that point, I agree with your discussion of what the question “How promising is a given cause area X rather than a cause area Y?” should be interpreted and roughly how it should be tackled.
I agree with this. I think one important consideration here is who are the agents for which we are doing the prioritization.
If our goal is to start a new charity and we are comparing causes, then all we should care about is the best intervention (we can find) - the one which we will end up implementing. If, in contrast, our goal is to develop a diverse community of people interested in exploring and solving some cause, we might care about a broader range of interventions, as well as potentially some qualities of the problem which help increase overall cohesiveness between the different actors
If, in contrast, our goal is to develop a diverse community of people interested in exploring and solving some cause, we might care about a broader range of interventions
I agree with this.
as well as potentially some qualities of the problem which help increase overall cohesiveness between the different actors
Sure. So, consider x-risk as an example cause area. It is a pretty broad cause area and contains secondary causes like mitigating AI-risk or Biorisk. Developing this as a common cause area involves advances like understanding what are the different risks, identifying relevant political and legal actions, making a strong ethical case, and gathering broad support.
So even if we think that the best interventions are likely in, say, AI-safety, it might be better to develop a community around a broader cause area. (So, here I’m thinking of cause area more like that in Givewell’s 2013 definition).
I think one important consideration here is who are the agents for which we are doing the prioritization.
If our goal is to start a new charity and we are comparing causes, then all we should care about is the best intervention (we can find) - the one which we will end up implementing.
This is a good point that I hadn’t thought of.
But I slightly disagree with charity example. The main reason is that the intervention that’s in general best may not be the one that’s best for whatever audience we’re talking to, due to personal fit. (In both cases, “best” should be interpreted as “best in expectation, on the margin, given our current knowledge and time available for searching”, but that’s irrelevant to the point I want to make.)
This is most obvious if we’re planning to ourselves run the charity. It’s less obvious if we’re doing something more like what Charity Entrepreneurship does, where we’ll ultimately seek out people from a large pool, since then we can seek people out partly based on personal fit for our charity idea. But:
our pool may still tend to be stronger in some areas than others, as is the case with EAs
if we have to optimise strongly for personal fit, we might have to sacrifice some degree of general competence/career capital/whatever, such that ultimately more good would’ve been done by a different founder running a charity that’s focused on an intervention that’d be less good in general (ignoring personal fit)
A smaller reason why I disagree is that, even if our primary goal is to start a new charity, it may be the case that a non-negligible fraction of the impact of our research comes from other effects (e.g., informing donors, researchers, people deciding on careers unrelated to charity entrepreneurship). This seems to be the case for Charity Entrepreneurship, and analogous things seem to be the case for 80,000 Hours, GiveWell, etc. But this point feels more like a nit-pick.
In any case, as mentioned, I do think that your point is a good one, and I think I only slightly disagree :)
Michael Plant made a similar point in the comment you cite at the start of this post. I responded that I didn’t think the point was quite right. The fleshed out picture of Michael’s reasoning given in this post resolves some of what I said in response (regarding not knowing in advance what the best interventions in each area are). But I think the reasoning given in this post still isn’t quite right, because I don’t think we only care about the best interventions in each area; I think we also care about other identifiable positive outliers. Reasons for that include the facts that:
We may be able to allocate enough resources to an area that the best would no longer be the best on the margin (if there are diminishing returns)
Some people may be sufficiently better fits for something else that that’s the best thing for them to do
And there are probably cases in which we have to or should “invest” in a cause area in a general way, not just invest in one specific intervention. So it’s useful to know which cause area will be able to best use a large chunk of a certain type of resources, not just which cause area contains the one intervention that is most cost-effective given generic resources on the current margin.
For example, let’s suppose for the sake of discussion that technical AI safety research is the best solution within the x-risk cause area, that deworming is the best solution in the global health & development cause area, and that technical AI safety is better than deworming. (Personally, I believe the third claim, and am more agnostic about the other two, but this is just an example.) In that case, in comparing the cause areas (to inform decisions like what skills EAs should skill up in, what networks we should build, what careers people should pursue, and where money should go), it would still be useful to know what the other frontrunner solutions are, and how they compare across cause areas.
I think this is, to some extent, a reason why GiveWell’s definition (mentioned at the end of this post) is useful.
Hmm. It seems like the only way this differs from my account is that ‘cause comparisons’ are/should be the comparison of the top interventions, rather than just intervention. But the ‘cause comparison’ is still impossible without (implicitly) evaluating the specific things you can do.
Yes, I think that sounds correct to me. I think that that’s what I was trying to get across with “But I think the reasoning given in this post still isn’t quite right, because I don’t think we only care about the best interventions in each area; I think we also care about other identifiable positive outliers.”
I.e., I do think that, other that that point, I agree with your discussion of what the question “How promising is a given cause area X rather than a cause area Y?” should be interpreted and roughly how it should be tackled.
I agree with this. I think one important consideration here is who are the agents for which we are doing the prioritization.
If our goal is to start a new charity and we are comparing causes, then all we should care about is the best intervention (we can find) - the one which we will end up implementing. If, in contrast, our goal is to develop a diverse community of people interested in exploring and solving some cause, we might care about a broader range of interventions, as well as potentially some qualities of the problem which help increase overall cohesiveness between the different actors
I agree with this.
I’m not sure I understand this. Could you expand?
Sure. So, consider x-risk as an example cause area. It is a pretty broad cause area and contains secondary causes like mitigating AI-risk or Biorisk. Developing this as a common cause area involves advances like understanding what are the different risks, identifying relevant political and legal actions, making a strong ethical case, and gathering broad support.
So even if we think that the best interventions are likely in, say, AI-safety, it might be better to develop a community around a broader cause area. (So, here I’m thinking of cause area more like that in Givewell’s 2013 definition).
This is a good point that I hadn’t thought of.
But I slightly disagree with charity example. The main reason is that the intervention that’s in general best may not be the one that’s best for whatever audience we’re talking to, due to personal fit. (In both cases, “best” should be interpreted as “best in expectation, on the margin, given our current knowledge and time available for searching”, but that’s irrelevant to the point I want to make.)
This is most obvious if we’re planning to ourselves run the charity. It’s less obvious if we’re doing something more like what Charity Entrepreneurship does, where we’ll ultimately seek out people from a large pool, since then we can seek people out partly based on personal fit for our charity idea. But:
our pool may still tend to be stronger in some areas than others, as is the case with EAs
if we have to optimise strongly for personal fit, we might have to sacrifice some degree of general competence/career capital/whatever, such that ultimately more good would’ve been done by a different founder running a charity that’s focused on an intervention that’d be less good in general (ignoring personal fit)
A smaller reason why I disagree is that, even if our primary goal is to start a new charity, it may be the case that a non-negligible fraction of the impact of our research comes from other effects (e.g., informing donors, researchers, people deciding on careers unrelated to charity entrepreneurship). This seems to be the case for Charity Entrepreneurship, and analogous things seem to be the case for 80,000 Hours, GiveWell, etc. But this point feels more like a nit-pick.
In any case, as mentioned, I do think that your point is a good one, and I think I only slightly disagree :)