I don’t share your optimistic view of research. You write:
it is reasonable to think that research would make progress because: Very little research has been done on this so far.
That’s because cause prioritization research is extremely difficult, not because no one has thought to do this.
Human history reflects positively on our ability to build a collective understanding of a difficult subject and eventually make headway.
Survivorship bias: what about all of the difficult subjects where we couldn’t make any progress and gave up?
Even if difficult, we should at least try! We would learn why such research is hard and should keep going until we reach a point of diminishing returns.
No, we should try if the expected returns are better than the next alternative. What if we’ve already hit diminishing returns?
More generally, research isn’t magic. Hiring a researcher and having them work 9-5 is no guarantee of solving a problem. You write:
What empirical evidence is there that we can reliably impact the long run trajectory of humanity and how have similar efforts gone in the past? [...]
I think there needs to be much better research into how to make complex decisions despite high uncertainty.
Isn’t it obvious that allocating researcher hours to these questions would be a waste of money? Almost by definition, we can’t have good evidence that we can impact the long-run (ie. centuries) trajectory of humanity, because we haven’t been collecting data for that long. And making complex decisions under high uncertainty will always be incredibly difficult; in the best case scenario, more research might yield small improvements in decision-making.
Hi Michael. Thank you for your points. It is good to hear opposing views. I have never worked in pure research so find it hard to judge and somewhat parroted Paul’s post. You may well be correct about the difficulty of research.
Let me try to draw from my own experience to elucidate why I may jumping to different intuitive conclusions on this question
My experience of research is from policy development. I think 2⁄3 of policy development is super easy and 1⁄3 is super difficult. The super easy stuff is just looking at the world and seeing if there are answers already out there and implementing them. For example on US police reform or UK tax policy or technology regulatory policy. We mostly know how to do these things well, we just need some incentive to implement best practice. The super difficult stuff is the foundational work, where a new problem emerges and no existing solutions abound, eg financial stability policy.
Now when I look at a question such as the one you quote of “much better research into how to make complex decisions despite high uncertainty” it seems to me to be a mix, but with definite areas that fall more towards the easy side. There appear to be a number of fields and domains with best practice that would be highly relevant to EAs making best decisions despite high uncertainty, that rarely seem to make it into EA circles. For example Enterprise Risk Management, economic models of Knightian uncertainty, organisational design, policy development toolkits, Robust Decision Making.
Maybe these have all been used and/or considered not relevant (I don’t work at GPI etc, I don’t know). But my life experience to date leaves me with an intuition that there is still low hanging research fruit just around the next corner. This is not a well-reasoned argument or a strong case simply me sharing where I come from and how I see the challenges and the path forward.
Thanks for the reply. I’m a jaded PhD student, but I am open to updating towards research-optimism.
I would distinguish research from implementation of research. I agree that there seems to be l0w-hanging fruit in implementing best practices, but I think implementation can be a super difficult problem in its own right. (See the state capacity literature.)
I don’t share your optimistic view of research. You write:
That’s because cause prioritization research is extremely difficult, not because no one has thought to do this.
Survivorship bias: what about all of the difficult subjects where we couldn’t make any progress and gave up?
No, we should try if the expected returns are better than the next alternative. What if we’ve already hit diminishing returns?
More generally, research isn’t magic. Hiring a researcher and having them work 9-5 is no guarantee of solving a problem. You write:
Isn’t it obvious that allocating researcher hours to these questions would be a waste of money? Almost by definition, we can’t have good evidence that we can impact the long-run (ie. centuries) trajectory of humanity, because we haven’t been collecting data for that long. And making complex decisions under high uncertainty will always be incredibly difficult; in the best case scenario, more research might yield small improvements in decision-making.
Hi Michael. Thank you for your points. It is good to hear opposing views. I have never worked in pure research so find it hard to judge and somewhat parroted Paul’s post. You may well be correct about the difficulty of research.
Let me try to draw from my own experience to elucidate why I may jumping to different intuitive conclusions on this question
My experience of research is from policy development. I think 2⁄3 of policy development is super easy and 1⁄3 is super difficult. The super easy stuff is just looking at the world and seeing if there are answers already out there and implementing them. For example on US police reform or UK tax policy or technology regulatory policy. We mostly know how to do these things well, we just need some incentive to implement best practice. The super difficult stuff is the foundational work, where a new problem emerges and no existing solutions abound, eg financial stability policy.
Now when I look at a question such as the one you quote of “much better research into how to make complex decisions despite high uncertainty” it seems to me to be a mix, but with definite areas that fall more towards the easy side. There appear to be a number of fields and domains with best practice that would be highly relevant to EAs making best decisions despite high uncertainty, that rarely seem to make it into EA circles. For example Enterprise Risk Management, economic models of Knightian uncertainty, organisational design, policy development toolkits, Robust Decision Making.
Maybe these have all been used and/or considered not relevant (I don’t work at GPI etc, I don’t know). But my life experience to date leaves me with an intuition that there is still low hanging research fruit just around the next corner. This is not a well-reasoned argument or a strong case simply me sharing where I come from and how I see the challenges and the path forward.
Thanks for the reply. I’m a jaded PhD student, but I am open to updating towards research-optimism.
I would distinguish research from implementation of research. I agree that there seems to be l0w-hanging fruit in implementing best practices, but I think implementation can be a super difficult problem in its own right. (See the state capacity literature.)