FWIW, I think it helps to think of effective altruism along the following lines. This is more or less taken from chapters 5 and 6 of my PhD thesis which got stuck into all this in tedious (and, in the end, rather futile) depth.
Who? As in, who are the beneficiary groups?
Options: people (in the near-term), animals (in the near-term), future sentient life
What? As in, what are the problems?
This gives you your cause areas, i.e. the problems you want to solve that directly benefit a particular group, e.g. poverty, factory farming, X-risks.
Effective altruism is a practical project, ultimately concerned about what the best actions are. To solve a problem requires thinking, at least implicitly, about particular solutions to those problems, so I think it’s basically a nonsense to try to compare “cause areas” without reference to specific things you can do, aka solutions. Hence, when we say we’re comparing “cause areas” what we are really doing is assessing the best solution in each cause area “bucket” and evaluating their cost-effectiveness. The most important cause = the one with the very most cost-effective intervention.
How? As if, how can the problems be best solved?
Here, I think it helps to distinguish between interventions and barriers. Interventions are the thing you do that ultimately solve the problem, e.g, cash transfers and bednets for helping those in poverty. You can then ask what are the barriers, i.e. the things that stop those interventions from being delivered. Is it because people don’t know about it? Do they want them but can’t afford them, etc? A solution removes a particular barrier to a particular intervention, e.g. just provides a bednet.
What’s confusing is where to fit in things like “improving rationality of decision-makers” and “growing the EA movement”, which people sometimes call causes. I think of these as ‘meta-causes’ because they indirect and diffusely work to remove the barrier to many of the ‘primary causes’, e.g. poverty.
It’s not clear we need answers to the ‘why?’, ‘when?‘, and ‘where?’ queries. Like I say, if you want to waste an hour or two, I slog through these issues in my thesis.
I think it’s basically a nonsense to try to compare “cause areas” without reference to specific things you can do, aka solutions. Hence, when we say we’re comparing “cause areas” what we are really doing is assessing the best solution in each cause area “bucket” and evaluating their cost-effectiveness. The most important cause = the one with the very most cost-effective intervention.
Maybe a minor point, but I don’t think this is quite right, because:
I don’t think we know what the best solution in each “bucket” is
I don’t think we have to in order to make educated guesses about which cause area will have the best solution, or will have the best “identifiable positive outliers” (or mean, or median, or upper quartile, or something like that)
I don’t think we only care about the best solution; 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
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[1] cause area, and that technical AI safety is better than deworming.[2] 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.
(Maybe you go into all that and more in your thesis, and just simplified a bit in your comment.)
[1] The fact that this is a reply to you made it salient to me that the term “global health & development” doesn’t clearly highlight the “wellbeing” angle. Would you call Happier Lives Institute’s cause area “global wellbeing”?
[2] Personally, I believe the third claim, and am more agnostic about the other two, but this is just an example.
FWIW, I think it helps to think of effective altruism along the following lines. This is more or less taken from chapters 5 and 6 of my PhD thesis which got stuck into all this in tedious (and, in the end, rather futile) depth.
Who? As in, who are the beneficiary groups?
Options: people (in the near-term), animals (in the near-term), future sentient life
What? As in, what are the problems?
This gives you your cause areas, i.e. the problems you want to solve that directly benefit a particular group, e.g. poverty, factory farming, X-risks.
Effective altruism is a practical project, ultimately concerned about what the best actions are. To solve a problem requires thinking, at least implicitly, about particular solutions to those problems, so I think it’s basically a nonsense to try to compare “cause areas” without reference to specific things you can do, aka solutions. Hence, when we say we’re comparing “cause areas” what we are really doing is assessing the best solution in each cause area “bucket” and evaluating their cost-effectiveness. The most important cause = the one with the very most cost-effective intervention.
How? As if, how can the problems be best solved?
Here, I think it helps to distinguish between interventions and barriers. Interventions are the thing you do that ultimately solve the problem, e.g, cash transfers and bednets for helping those in poverty. You can then ask what are the barriers, i.e. the things that stop those interventions from being delivered. Is it because people don’t know about it? Do they want them but can’t afford them, etc? A solution removes a particular barrier to a particular intervention, e.g. just provides a bednet.
What’s confusing is where to fit in things like “improving rationality of decision-makers” and “growing the EA movement”, which people sometimes call causes. I think of these as ‘meta-causes’ because they indirect and diffusely work to remove the barrier to many of the ‘primary causes’, e.g. poverty.
It’s not clear we need answers to the ‘why?’, ‘when?‘, and ‘where?’ queries. Like I say, if you want to waste an hour or two, I slog through these issues in my thesis.
I like this answer.
Maybe a minor point, but I don’t think this is quite right, because:
I don’t think we know what the best solution in each “bucket” is
I don’t think we have to in order to make educated guesses about which cause area will have the best solution, or will have the best “identifiable positive outliers” (or mean, or median, or upper quartile, or something like that)
I don’t think we only care about the best solution; 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
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[1] cause area, and that technical AI safety is better than deworming.[2] 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.
(Maybe you go into all that and more in your thesis, and just simplified a bit in your comment.)
[1] The fact that this is a reply to you made it salient to me that the term “global health & development” doesn’t clearly highlight the “wellbeing” angle. Would you call Happier Lives Institute’s cause area “global wellbeing”?
[2] Personally, I believe the third claim, and am more agnostic about the other two, but this is just an example.