Forecasting is a common good to many causes, so you’d expect it not to be neglected. But in practice, it seems the only people working on forecasting are EA or EA-adjacent (I’d count Tetlock as adjacent)
I think I’ve become a bit convinced that incentive and coordination problems are so poor that many “common goods” are surprisingly neglected. The history of the slow development and proliferation of Bayesian techniques in general (up to around 20 years ago maybe, but even now I think the foundations can be improved a lot) seems quite awful.
Also, at this point, I feel quite strong about much of the EA community; like we’ve gathered up many of the most [intelligent + pragmatic + agentic + high-level-optimizing] people in the world. As such I think we can compete and do a good job in many areas we may choose to focus on. So it could be that we could move up from “absolutely, incredibly neglected”, to “just somewhat neglected”, which could open up a whole bunch of fields.
like we’ve gathered up many of the most [intelligent + pragmatic + agentic + high-level-optimizing] people in the world
It seems like I routinely learn about some smart and insightful person through non-EA channels and then later find out they’re involved in EA or at least subscribe to EA principles—most recent example for me is Gordon Irlam, who I originally learned about through his writings on portfolio selection.
I’ve been thinking a lot about the lack of non-EA interest or focus on forecasting or related tools. I was very surprised when I made Guesstimate and there was both excitement from several people, but not that much excitement from most businesses or governments.
I think that forecasting of the GJP sort is still highly niche. Almost no one knows of it or understands the value. You can look at this as similar to specific advances in, say, type theory or information theory.
The really smart groups that have interests in improving their long term judgement seem to be financial institutions and similar. These are both highly secretive, and not interested in spending extra effort helping outside groups.
So to really advance a field like judgemental forecasting would require a combination of expertise, funding, and interest in helping the broad public, and this is a highly unusual combination. I imagine that if IARPA wasn’t around in time to both be interested in and able to fund GJP’s efforts, much less would have happened there. I’d also personally point out that I’d expect that IARPA’s funding of it was around 1/3rd or maybe 1/20th as efficient as it would have been if OpenPhil would have organized a more directed effort, in terms of global benefit.
This makes me think that there are probably many other very specific technology and research efforts that also be exciting for us to focus on, but we don’t have the expertise to recognize them. May may have gotten lucky with forecasting/estimation tech, as that was something we had to get close to anyway for other reasons.
Also worth noting that the managing director of IARPA’s forecasting program was Jason Matheny, who previously founded New Harvest (which does cultured meat research, and was the first such org AFAIK) and did x-risk research at FHI.
I think I’ve become a bit convinced that incentive and coordination problems are so poor that many “common goods” are surprisingly neglected. The history of the slow development and proliferation of Bayesian techniques in general (up to around 20 years ago maybe, but even now I think the foundations can be improved a lot) seems quite awful.
Also, at this point, I feel quite strong about much of the EA community; like we’ve gathered up many of the most [intelligent + pragmatic + agentic + high-level-optimizing] people in the world. As such I think we can compete and do a good job in many areas we may choose to focus on. So it could be that we could move up from “absolutely, incredibly neglected”, to “just somewhat neglected”, which could open up a whole bunch of fields.
It seems like I routinely learn about some smart and insightful person through non-EA channels and then later find out they’re involved in EA or at least subscribe to EA principles—most recent example for me is Gordon Irlam, who I originally learned about through his writings on portfolio selection.
I’ve been thinking a lot about the lack of non-EA interest or focus on forecasting or related tools. I was very surprised when I made Guesstimate and there was both excitement from several people, but not that much excitement from most businesses or governments.
I think that forecasting of the GJP sort is still highly niche. Almost no one knows of it or understands the value. You can look at this as similar to specific advances in, say, type theory or information theory.
The really smart groups that have interests in improving their long term judgement seem to be financial institutions and similar. These are both highly secretive, and not interested in spending extra effort helping outside groups.
So to really advance a field like judgemental forecasting would require a combination of expertise, funding, and interest in helping the broad public, and this is a highly unusual combination. I imagine that if IARPA wasn’t around in time to both be interested in and able to fund GJP’s efforts, much less would have happened there. I’d also personally point out that I’d expect that IARPA’s funding of it was around 1/3rd or maybe 1/20th as efficient as it would have been if OpenPhil would have organized a more directed effort, in terms of global benefit.
This makes me think that there are probably many other very specific technology and research efforts that also be exciting for us to focus on, but we don’t have the expertise to recognize them. May may have gotten lucky with forecasting/estimation tech, as that was something we had to get close to anyway for other reasons.
Also worth noting that the managing director of IARPA’s forecasting program was Jason Matheny, who previously founded New Harvest (which does cultured meat research, and was the first such org AFAIK) and did x-risk research at FHI.
Yep, and a few others at IARPA who worked around the forecasting stuff were also EAs or close.