Thanks, I definitely agree that there should be more prioritization research. (I work at GPI, so maybe that’s predictable.) And I agree that for all the EA talk about how important it is, there’s surprisingly little really being done.
One point I’d like to raise, though: I don’t know what you’re looking for exactly, but my impression is that good prioritization research will in general not resemble what EA people usually have in mind when they talk about “cause prioritization”. So when putting together an overview like this, one might overlook some of even what little prioritization research is being done.
In my experience, people usually imagine a process of explicitly listing causes, thinking through and evaluating the consequences of working in each of them, and then ranking the results (kind of like GiveWell does with global poverty charities). I expect that the main reason more of this doesn’t exist is that, when people try to start doing this, they typically conclude it isn’t actually the most helpful way to shed light on which cause EA actors should focus on.
I think that, more often than not, a more helpful way to go about prioritizing is to build a model of the world, just rich enough to represent all the levers between which you’re considering and the ways you expect them to interact, and then to see how much better the world gets when you divide your resources among the levers this way or that. By analogy, a “naïve” government’s approach to prioritizing between, say, increasing this year’s GDP and decreasing this year’s carbon emissions would be to try to account explicitly for the consequences of each and to compare them. Taking the lowering emissions side, this will produce a tangled web of positive and negative consequences, which interact heavily both with each other and with the consequences of increasing GDP: it will mean
less consumption this year,
less climate damage next year,
less accumulated capital next year with which to mitigate climate damage,
more of an incentive for people next year to allow more emissions,
more predictable weather and therefore easier production next year,
…but this might mean more (or less) emissions next year,
…and so on.
It quickly becomes clear that finishing the list and estimating all its items is hopeless. So what people do instead is write down an “integrated assessment model”. What the IAM is ultimately modeling, albeit in very low resolution, is the whole world, with governments, individuals, and various economic and environmental moving parts behaving in a way that straightforwardly gives rise to the web of interactions that would appear on that infinitely long list. Then, if you’re, say, a government in 2020, you just solve for the policy—the level of the carbon cap, the level of green energy subsidization, and whatever else the model allows you to consider—that maximizes your objective function, whatever that may be. What comes out of the model will be sensitive to the construction of the model, of course, and so may not be very informative. But I’d say it will be at least as informative as an attempt to do something that looks more like what people sometimes seem to mean by cause prioritization.
If the project of “writing down stylized models of the world and solving for the optimal thing for EAs to do in them” counts as cause prioritization, I’d say two projects I’ve had at least some hand in over the past year count: (at least sections 4 and 5.1 of) my own paper on patient philanthropy and (at least section 6.3 of) Leopold Aschenbrenner’s paper on existential risk and growth. Anyway, I don’t mean to plug these projects in particular, I just want to make the case that they’re examples of a class of work that is being done to some extent and that should count as prioritization research.
…And examples of what GPI will hopefully soon be fostering more of, for whatever that’s worth! It’s all philosophy so far, I know, but my paper and Leo’s are going on the GPI website once they’re just a bit more polished. And we’ve just hired two econ postdocs I’m really excited about, so we’ll see what they come up with.
Hey Phil. I’m someone who is very interested in the work of GPI and am impressed by what I have seen so far. I’m looking forward to seeing what the new economists get up to!
I had a look at Leopold’s paper a while back, have listened to you on the 80K podcast and have watched a few of GPI’s videos including Christian Tarsney’s one on the epistemic challenge to longtermism. I notice that in a lot of this research, key results are highly sensitive to the value of certain parameters. My memory is slightly hazy on specifics but I think in Christian’s paper the validity of longtermism depends largely on the existence and frequency of exogenous nullifying events (ENEs) that can essentially wipe out any trajectory change efforts that came before (apologies if I’m not being perfectly accurate here).
I am wondering if empirical estimation of key parameters is a gap in current cause prioritisation research. Because the value of these parameters is so important in determining results from the models, it seems very high-value to more accurately estimate these parameters. Do you know if anyone is actually doing this? Is anyone for example looking into the nature of ENEs? Is this something new economists at GPI might engage in? If this type of research isn’t suitable for GPI, does GPI need closer links to other research institutions that are interested in carrying out more empirical research?
Thanks! I agree that people in EA—including Christian, Leopold, and myself—have done a fair bit of theory/modeling work at this point which would benefit from relevant empirical work. I don’t think this is what either of the current new economists will engage in anytime soon, unfortunately. But I don’t think it would be outside a GPI economist’s remit, especially once we’ve grown.
OK that’s good to hear. It probably makes sense to spend some time laying a solid theoretical base to build on. I’m aware of how new GPI still is so I’m looking forward to seeing how things progress!
Hi, Thank you for this really helpful comment. It was really interesting to read about how you work on cause prioritisation research and use IAMs. Glad that GPI will be expanding.
“writing down stylized models of the world and solving for the optimal thing for EAs to do in them”
I think this is one of the most important things we can be doing. Maybe even the most important since it covers such a wide area and so much government policy is so far from optimal.
you just solve for the policy … that maximizes your objective function, whatever that may be.
Societies need many distinct systems: a transport system, a school system, etc. These systems cannot be justified if they are amoral, so they must serve morality. Each system cannot, however, achieve the best moral outcome on its own: If your transport system doesn’t cure cancer, it probably isn’t doing everything you want; if it does cure cancer, it isn’t just a “transport” system...
Unless by policy, you mean “the entirety of what government does”, then yes. But given that you’re going to consider one area at a time, and you’re “only including all the levers between which you’re considering”, you could reach a local optimum rather than a truly ideal end state. The way I like to think about it is “How would a system for prisons (for example) be in the best possible future?” This is not necessarily going to be the system that does the greatest good at the margin when constrained to the domain you’re considering (though they often are). Rather than think about a system maximizing your objective function, it’s better to think of systems as satisfying goals that are aligned with your objective function.
Thanks, I definitely agree that there should be more prioritization research. (I work at GPI, so maybe that’s predictable.) And I agree that for all the EA talk about how important it is, there’s surprisingly little really being done.
One point I’d like to raise, though: I don’t know what you’re looking for exactly, but my impression is that good prioritization research will in general not resemble what EA people usually have in mind when they talk about “cause prioritization”. So when putting together an overview like this, one might overlook some of even what little prioritization research is being done.
In my experience, people usually imagine a process of explicitly listing causes, thinking through and evaluating the consequences of working in each of them, and then ranking the results (kind of like GiveWell does with global poverty charities). I expect that the main reason more of this doesn’t exist is that, when people try to start doing this, they typically conclude it isn’t actually the most helpful way to shed light on which cause EA actors should focus on.
I think that, more often than not, a more helpful way to go about prioritizing is to build a model of the world, just rich enough to represent all the levers between which you’re considering and the ways you expect them to interact, and then to see how much better the world gets when you divide your resources among the levers this way or that. By analogy, a “naïve” government’s approach to prioritizing between, say, increasing this year’s GDP and decreasing this year’s carbon emissions would be to try to account explicitly for the consequences of each and to compare them. Taking the lowering emissions side, this will produce a tangled web of positive and negative consequences, which interact heavily both with each other and with the consequences of increasing GDP: it will mean
less consumption this year,
less climate damage next year,
less accumulated capital next year with which to mitigate climate damage,
more of an incentive for people next year to allow more emissions,
more predictable weather and therefore easier production next year,
…but this might mean more (or less) emissions next year,
…and so on.
It quickly becomes clear that finishing the list and estimating all its items is hopeless. So what people do instead is write down an “integrated assessment model”. What the IAM is ultimately modeling, albeit in very low resolution, is the whole world, with governments, individuals, and various economic and environmental moving parts behaving in a way that straightforwardly gives rise to the web of interactions that would appear on that infinitely long list. Then, if you’re, say, a government in 2020, you just solve for the policy—the level of the carbon cap, the level of green energy subsidization, and whatever else the model allows you to consider—that maximizes your objective function, whatever that may be. What comes out of the model will be sensitive to the construction of the model, of course, and so may not be very informative. But I’d say it will be at least as informative as an attempt to do something that looks more like what people sometimes seem to mean by cause prioritization.
If the project of “writing down stylized models of the world and solving for the optimal thing for EAs to do in them” counts as cause prioritization, I’d say two projects I’ve had at least some hand in over the past year count: (at least sections 4 and 5.1 of) my own paper on patient philanthropy and (at least section 6.3 of) Leopold Aschenbrenner’s paper on existential risk and growth. Anyway, I don’t mean to plug these projects in particular, I just want to make the case that they’re examples of a class of work that is being done to some extent and that should count as prioritization research.
…And examples of what GPI will hopefully soon be fostering more of, for whatever that’s worth! It’s all philosophy so far, I know, but my paper and Leo’s are going on the GPI website once they’re just a bit more polished. And we’ve just hired two econ postdocs I’m really excited about, so we’ll see what they come up with.
Hey Phil. I’m someone who is very interested in the work of GPI and am impressed by what I have seen so far. I’m looking forward to seeing what the new economists get up to!
I had a look at Leopold’s paper a while back, have listened to you on the 80K podcast and have watched a few of GPI’s videos including Christian Tarsney’s one on the epistemic challenge to longtermism. I notice that in a lot of this research, key results are highly sensitive to the value of certain parameters. My memory is slightly hazy on specifics but I think in Christian’s paper the validity of longtermism depends largely on the existence and frequency of exogenous nullifying events (ENEs) that can essentially wipe out any trajectory change efforts that came before (apologies if I’m not being perfectly accurate here).
I am wondering if empirical estimation of key parameters is a gap in current cause prioritisation research. Because the value of these parameters is so important in determining results from the models, it seems very high-value to more accurately estimate these parameters. Do you know if anyone is actually doing this? Is anyone for example looking into the nature of ENEs? Is this something new economists at GPI might engage in? If this type of research isn’t suitable for GPI, does GPI need closer links to other research institutions that are interested in carrying out more empirical research?
Thanks! I agree that people in EA—including Christian, Leopold, and myself—have done a fair bit of theory/modeling work at this point which would benefit from relevant empirical work. I don’t think this is what either of the current new economists will engage in anytime soon, unfortunately. But I don’t think it would be outside a GPI economist’s remit, especially once we’ve grown.
OK that’s good to hear. It probably makes sense to spend some time laying a solid theoretical base to build on. I’m aware of how new GPI still is so I’m looking forward to seeing how things progress!
Hi, Thank you for this really helpful comment. It was really interesting to read about how you work on cause prioritisation research and use IAMs. Glad that GPI will be expanding.
I think this is one of the most important things we can be doing. Maybe even the most important since it covers such a wide area and so much government policy is so far from optimal.
I don’t think that’s right. I’ve written about what it means for a system to do “the optimal thing” and the answer cannot be that a single policy maximizes your objective function:
Unless by policy, you mean “the entirety of what government does”, then yes. But given that you’re going to consider one area at a time, and you’re “only including all the levers between which you’re considering”, you could reach a local optimum rather than a truly ideal end state. The way I like to think about it is “How would a system for prisons (for example) be in the best possible future?” This is not necessarily going to be the system that does the greatest good at the margin when constrained to the domain you’re considering (though they often are). Rather than think about a system maximizing your objective function, it’s better to think of systems as satisfying goals that are aligned with your objective function.
I wonder if we could create an open source library of IAMs for researchers and EAs to use and audit.
At a glance, Salesforce’s AI Economist seems like an attempted implementation of an IAM.