“I have no particular reason to disagree with any of your reasoning about how promising this idea is for the population it intends to benefit, but I just think benefitting that population is [huge number] less important than benefitting [this other population], so I really don’t care.”
So suppose that the intervention was about cows, and I (the vectors in “1” in the image) gave them some moderate weight X, the length of the red arrow. Then if someone gives them a weight of 0.0001X, their red arrow becomes much smaller (as in 2.), and the total volume enclosed by their cube becomes smaller. I’m thinking that the volume represents promisingness. But they can just apply that division X → 0.0001X to all my ratings, and calculate their new volumes and ratings (which will be different from mine, because cause areas which only affect, say, humans, won’t be affected).
Or “I have no particular reason to disagree with any of your reasoning about how promising this idea is if we aim to simply maximise expected utility even in Pascalian situations, and accept long chains of reasoning with limited empirical data behind them. But I’m just really firmly convinced that those are bad ways to make decisions and form beliefs, so I don’t really care how well the idea performs from that perspective.”
In this case, the red arrow would go completely to 0, and that person would just focus on the area of the square in which the blue and green arrows lie, across all cause candidates. Because I am looking at volume and they are looking at areas, our ratings will again differ.
This cube approach is interesting, but my instinctive response is to agree with MichaelA, if someone doesn’t think influencing the long-run future is tractable then they will probably just want to entirely filter out longtermist cause areas from the very start and focus on shorttermist areas. I’m not sure comparing areas/volumes between shorttermist and longtermist areas will be something they will be that interested in doing. My feeling is the cube approach may be over complicating things.
If I were doing this myself, or starting an ’exploratory altruism’ organisation similar to the one Charity Entrepreneurship is thinking about starting, I would probably take one of the following two approaches:
Similar to 80,000 Hours, just decide what the most important class of cause areas is to focus on at the current margin and ignore everything else. 80K has decided to focus on longtermist cause areas and has outlined clearly why they are doing this (key ideas page has a decent overview). So people know what they are getting from 80K and 80K can freely assume totalism, vastness of the future etc. when they are carrying out their research. The drawback of this approach is it alienates a lot of people, as evidenced by the founding of a new careers org, ‘Probably Good’.
Try to please everyone by carrying out multiple distinct funnelling exercises, one for each class of cause area (say near-term human welfare, near-term animal welfare, x-risk, non-x-risk longtermist areas). Each funnelling exercise would make different foundational assumptions according to that cause area. People could then just choose which funnelling exercise to pay attention to and, in theory, everybody wins. The drawback to this approach that 80K would state is that it probably means spending a lot of time focusing on cause areas that you don’t think are actually that high value, which may just be very inefficient.
I think this decision is tough, but on balance I would probably go for option 1 and would focus on longtermist cause areas, in part because shorttermist areas have historically been given much more thought so there is probably less meaningful progress that can be made there.
Then if someone gives them a weight of 0.0001X, their red arrow becomes much smaller (as in 2.), and the total volume enclosed by their cube becomes smaller.
Yeah, I agree with this.
What I’m saying is that, if they and you disagree sufficiently much on 1 factor in a way that they already know about before this process starts, they might justifiably be confident that this adjustment will mean any ideas in category A (e.g., things focused on helping cows) will be much less promising than some ideas in category B (e.g., things focused on helping humans in the near-term, or things focused on beings in the long-term).
And then they might justifiably be confident that your evaluations of ideas in category A won’t be very useful to them (and thus aren’t worth reading, aren’t worth funding you to make, etc.)
I think this is basically broadly the same sort of reasoning that leads GiveWell to rule out many ideas (e.g., those that focus on benefitting developed-world populations) before even doing shallow reviews. Those ideas could vary substantially on many dimensions GiveWell cares about, but they still predict that almost all of the ideas that are best by their lights would be found in a different category that can be known already to typically be much higher on some other dimensions (I guess neglectedness, in this case).
(I haven’t followed GiveWell’s work very closely for a while, so I may be misrepresenting things.)
(All of this will not be the case if a person is more uncertain about e.g. which population group it’s best to benefit or which epistemic approaches should be used. So, e.g., the ratings for near-term animal welfare focused ideas should still be of interest for some portion of longtermism-leaning people.)
In this case, the red arrow would go completely to 0, and that person would just focus on the area of the square in which the blue and green arrows lie, across all cause candidates. Because I am looking at volume and they are looking at areas, our ratings will again differ.
I also agree with this. Again, I’d just say that some ideas might only warrant attention if we do care about the red arrow—we might be able to predict in advance that almost all of the ideas with the largest “areas” (rather than “volumes”) would not be in that category. If so, then people might have reason to not pay attention to your other ratings for those ideas, because their time is limited and they should look elsewhere if they just want high-area ideas.
Another way to frame this would be in terms of crucial considerations: “a consideration such that if it were taken into account it would overturn the conclusions we would otherwise reach about how we should direct our efforts, or an idea or argument that might possibly reveal the need not just for some minor course adjustment in our practical endeavors but a major change of direction or priority.”
A quick example: If Alice currently thinks that a 1 percentage point reduction in existential risk is many orders of magnitude more important than a 1 percentage point increase in the average welfare of people in developing nations*, then I think looking at ratings from this sort of system for ideas focused on improving welfare of people in developing nations is not a good use of Alice’s time.
I think she’d use that time better by doing things like:
looking at ratings of ideas focused on reducing existential risk
looking at ideas focused on proxies that seem more connected to reducing existential risk
looking specifically at crucial-consideration-y things like “How does improving welfare of people in developing nations affect existential risk?” or “What are the strongest arguments for focusing on welfare in developing nations rather than on existential risk”
This wouldn’t be aided much by answers to questions like “Has [idea X] been implemented yet? How costly would it be? What is the evidence that it indeed achieves its stated objective?”
See also Charity Entrepreneurship’s “supporting reports”, which “focus on meta and cross-cutting issues that affect a large number of ideas and would not get covered by our standard reports. Their goal is to support the consideration of different ideas.”
*I chose those proxies and numbers fairly randomly.
To be clear: I am not saying that I don’t think your model, or the sort of work that’s sort-of proposed by the model, wouldn’t be valuable. I think it would be valuable. I’m just explaining why I think some portions of the work won’t be particularly valuable to some portion of EAs. (Just as most of GiveWell’s work or FHI’s work isn’t particularly valuable—at least on the object level—to some EAs.)
So suppose that the intervention was about cows, and I (the vectors in “1” in the image) gave them some moderate weight X, the length of the red arrow. Then if someone gives them a weight of 0.0001X, their red arrow becomes much smaller (as in 2.), and the total volume enclosed by their cube becomes smaller. I’m thinking that the volume represents promisingness. But they can just apply that division X → 0.0001X to all my ratings, and calculate their new volumes and ratings (which will be different from mine, because cause areas which only affect, say, humans, won’t be affected).
In this case, the red arrow would go completely to 0, and that person would just focus on the area of the square in which the blue and green arrows lie, across all cause candidates. Because I am looking at volume and they are looking at areas, our ratings will again differ.
This cube approach is interesting, but my instinctive response is to agree with MichaelA, if someone doesn’t think influencing the long-run future is tractable then they will probably just want to entirely filter out longtermist cause areas from the very start and focus on shorttermist areas. I’m not sure comparing areas/volumes between shorttermist and longtermist areas will be something they will be that interested in doing. My feeling is the cube approach may be over complicating things.
If I were doing this myself, or starting an ’exploratory altruism’ organisation similar to the one Charity Entrepreneurship is thinking about starting, I would probably take one of the following two approaches:
Similar to 80,000 Hours, just decide what the most important class of cause areas is to focus on at the current margin and ignore everything else. 80K has decided to focus on longtermist cause areas and has outlined clearly why they are doing this (key ideas page has a decent overview). So people know what they are getting from 80K and 80K can freely assume totalism, vastness of the future etc. when they are carrying out their research. The drawback of this approach is it alienates a lot of people, as evidenced by the founding of a new careers org, ‘Probably Good’.
Try to please everyone by carrying out multiple distinct funnelling exercises, one for each class of cause area (say near-term human welfare, near-term animal welfare, x-risk, non-x-risk longtermist areas). Each funnelling exercise would make different foundational assumptions according to that cause area. People could then just choose which funnelling exercise to pay attention to and, in theory, everybody wins. The drawback to this approach that 80K would state is that it probably means spending a lot of time focusing on cause areas that you don’t think are actually that high value, which may just be very inefficient.
I think this decision is tough, but on balance I would probably go for option 1 and would focus on longtermist cause areas, in part because shorttermist areas have historically been given much more thought so there is probably less meaningful progress that can be made there.
Yeah, I agree with this.
What I’m saying is that, if they and you disagree sufficiently much on 1 factor in a way that they already know about before this process starts, they might justifiably be confident that this adjustment will mean any ideas in category A (e.g., things focused on helping cows) will be much less promising than some ideas in category B (e.g., things focused on helping humans in the near-term, or things focused on beings in the long-term).
And then they might justifiably be confident that your evaluations of ideas in category A won’t be very useful to them (and thus aren’t worth reading, aren’t worth funding you to make, etc.)
I think this is basically broadly the same sort of reasoning that leads GiveWell to rule out many ideas (e.g., those that focus on benefitting developed-world populations) before even doing shallow reviews. Those ideas could vary substantially on many dimensions GiveWell cares about, but they still predict that almost all of the ideas that are best by their lights would be found in a different category that can be known already to typically be much higher on some other dimensions (I guess neglectedness, in this case).
(I haven’t followed GiveWell’s work very closely for a while, so I may be misrepresenting things.)
(All of this will not be the case if a person is more uncertain about e.g. which population group it’s best to benefit or which epistemic approaches should be used. So, e.g., the ratings for near-term animal welfare focused ideas should still be of interest for some portion of longtermism-leaning people.)
I also agree with this. Again, I’d just say that some ideas might only warrant attention if we do care about the red arrow—we might be able to predict in advance that almost all of the ideas with the largest “areas” (rather than “volumes”) would not be in that category. If so, then people might have reason to not pay attention to your other ratings for those ideas, because their time is limited and they should look elsewhere if they just want high-area ideas.
Another way to frame this would be in terms of crucial considerations: “a consideration such that if it were taken into account it would overturn the conclusions we would otherwise reach about how we should direct our efforts, or an idea or argument that might possibly reveal the need not just for some minor course adjustment in our practical endeavors but a major change of direction or priority.”
A quick example: If Alice currently thinks that a 1 percentage point reduction in existential risk is many orders of magnitude more important than a 1 percentage point increase in the average welfare of people in developing nations*, then I think looking at ratings from this sort of system for ideas focused on improving welfare of people in developing nations is not a good use of Alice’s time.
I think she’d use that time better by doing things like:
looking at ratings of ideas focused on reducing existential risk
looking at ideas focused on proxies that seem more connected to reducing existential risk
looking specifically at crucial-consideration-y things like “How does improving welfare of people in developing nations affect existential risk?” or “What are the strongest arguments for focusing on welfare in developing nations rather than on existential risk”
This wouldn’t be aided much by answers to questions like “Has [idea X] been implemented yet? How costly would it be? What is the evidence that it indeed achieves its stated objective?”
See also Charity Entrepreneurship’s “supporting reports”, which “focus on meta and cross-cutting issues that affect a large number of ideas and would not get covered by our standard reports. Their goal is to support the consideration of different ideas.”
*I chose those proxies and numbers fairly randomly.
To be clear: I am not saying that I don’t think your model, or the sort of work that’s sort-of proposed by the model, wouldn’t be valuable. I think it would be valuable. I’m just explaining why I think some portions of the work won’t be particularly valuable to some portion of EAs. (Just as most of GiveWell’s work or FHI’s work isn’t particularly valuable—at least on the object level—to some EAs.)
Makes sense, thanks