Hi Sam, the key passage was in this text from the outset of the “Top Institutions” section:
Furthermore, over 70% of the total opportunity for x-risk reduction was concentrated in the top ten of those 41 institutions, and many of those in the next tier are subdivisions, competitors, or otherwise closely related to the top-ranked entities.
While we don’t have a similar global estimate of the total wellbeing-adjusted life-years at stake in any given year, we similarly observed a level of concentration among the top ten scorers in that category of about 70%.
Granted, we are only talking about the 41 organizations that were included in the model, but having spent some time with those numbers, I would be at least somewhat surprised if the 70% figure were to go down by all that much (say, below 55%) if we expanded the analysis to our entire short list of 77 organizations, and so on and so forth.
However, I would say that my overall credence in the claim that you quoted is moderately low, which was intended to be conveyed by the use of the verb “suggest” rather than a stronger word. Probably a better way to characterize it is that it’s something like a working hypothesis that I believe to be true based on this analysis but am still seeking to confirm in a larger sense.
Regarding your specific counterarguments:
More public and powerful organizations being harder to influence—this assumption is already taken into account in our analysis, albeit with a lot of uncertainty around the relevant variables.
Path dependencies—I think you are right about the general point, but I don’t think it really challenges the claim about concentration of opportunity. The basic case we’re talking about is when you invest in one or a few demonstration projects with lower-profile institutions, where the hope is that this will help grease the wheels for spreading the idea or intervention being demonstrated to a higher-profile institution where it matters more. I think if we’re serious about that being the goal, it probably makes more sense to view those investments as part of a longer-term strategy targeting the higher-profile institution, and one could even budget for the demonstration projects as part of the same $100M (or whatever amount) philanthropic investment.
Non-targeted institutional reforms—yes, this is along the lines of what I called the “product-centered” theory of change in the “Insights and Takeaways” section. What I understand you to be saying is that even if I’m right that there is a significant relative concentration of reform opportunities on an institution-by-institution basis, it doesn’t necessarily imply that the most promising interventions targeting specific institutions will perform better on a per-dollar cost-effectiveness basis than the most promising interventions that don’t. This is a good point and I’m glad you raised it.
I still expect we would if have some disagreement on how likely it is for this concentrated opportunities hypothesis to be true.
An interesting cheap (but low veracity) test of this hypothesis that could be to list out a handful of institutions (or collections of institutions) that you think would certainly NOT be considered as the “most powerful” but might matter (E.g.: university career services, EA community institutions, the Biological Weapons Convention implementation unit, AI/tech regulatory bodies, top business schools, tech start-ups, etc, etc) and then include a few of those in your modelling exercise and see how $100m to improve institutions in those cases compares to $100m to influence the most powerful institutions.
On the “path dependencies” point you make – I largely agree. I just think there is a risk (which I am sure you are already thinking through) that if you overly focus future research on just the most powerful institutions you might miss out on finding these indirect opportunities.
Hi Sam, the key passage was in this text from the outset of the “Top Institutions” section:
Granted, we are only talking about the 41 organizations that were included in the model, but having spent some time with those numbers, I would be at least somewhat surprised if the 70% figure were to go down by all that much (say, below 55%) if we expanded the analysis to our entire short list of 77 organizations, and so on and so forth.
However, I would say that my overall credence in the claim that you quoted is moderately low, which was intended to be conveyed by the use of the verb “suggest” rather than a stronger word. Probably a better way to characterize it is that it’s something like a working hypothesis that I believe to be true based on this analysis but am still seeking to confirm in a larger sense.
Regarding your specific counterarguments:
More public and powerful organizations being harder to influence—this assumption is already taken into account in our analysis, albeit with a lot of uncertainty around the relevant variables.
Path dependencies—I think you are right about the general point, but I don’t think it really challenges the claim about concentration of opportunity. The basic case we’re talking about is when you invest in one or a few demonstration projects with lower-profile institutions, where the hope is that this will help grease the wheels for spreading the idea or intervention being demonstrated to a higher-profile institution where it matters more. I think if we’re serious about that being the goal, it probably makes more sense to view those investments as part of a longer-term strategy targeting the higher-profile institution, and one could even budget for the demonstration projects as part of the same $100M (or whatever amount) philanthropic investment.
Non-targeted institutional reforms—yes, this is along the lines of what I called the “product-centered” theory of change in the “Insights and Takeaways” section. What I understand you to be saying is that even if I’m right that there is a significant relative concentration of reform opportunities on an institution-by-institution basis, it doesn’t necessarily imply that the most promising interventions targeting specific institutions will perform better on a per-dollar cost-effectiveness basis than the most promising interventions that don’t. This is a good point and I’m glad you raised it.
I still expect we would if have some disagreement on how likely it is for this concentrated opportunities hypothesis to be true.
An interesting cheap (but low veracity) test of this hypothesis that could be to list out a handful of institutions (or collections of institutions) that you think would certainly NOT be considered as the “most powerful” but might matter (E.g.: university career services, EA community institutions, the Biological Weapons Convention implementation unit, AI/tech regulatory bodies, top business schools, tech start-ups, etc, etc) and then include a few of those in your modelling exercise and see how $100m to improve institutions in those cases compares to $100m to influence the most powerful institutions.
On the “path dependencies” point you make – I largely agree. I just think there is a risk (which I am sure you are already thinking through) that if you overly focus future research on just the most powerful institutions you might miss out on finding these indirect opportunities.