Should forecasting receive more or less EA funding?
I think it’s clear from Marcus experience and his argument that forecasting per se failed to produce results. There are many adjacent areas that are related to better epistemology and decision making and the money should be rationally diverted there.
As an applied futurologist specializing in human augmentation and management of innovation I can “push my favorite solutions” here, but my position is more general—find better target for this funding.
This obviously assumes Marcus has a sufficient level of experience to justify the claims. Which I think, given other comments, can be adequately challenged.
It would be good to know what metric/threshold/examples would be taken as forecasting delivering adequate impact to justify funding. From examples in this thread alone, we can see senior government decision makers in both the U.K. (including Ministerial teams and critical committees) and US, frontier labs safety teams, and philanthropic funds moving tens of millions of dollars a year) have utilised forecasting (either the process or the outputs) to inform their decisions.
The argument of it only shifting a decision 1-2% is totally fair. But to keep consistent I’d expect the same people who make that argument to also be highly sceptical of the vast majority of research funding.
Marcus’s experience can be questioned and his position challenged, but in other comments other knowledgeable and experienced people supported some of his arguments, even though they were objecting to others. So I would say that in general Marcus’s position is strong. It’s clear it’s provocative, but I don’t see a problem with that personally.
The metrics could be chosen based on your overall decision making system. The end points are measured by NPV, QLY, etc. It’s clear that you need some intermediate metrics, of course, which I would say, is the number and scale of decisions where forecasting not only “informed” the decisions, but “determined”.
Examples from determining the impact of scientific research: lead is bad ⇒ leaded gasoline banned. CO2 is bad ⇒ fight global warming.
I would ideally like to see something similar (obviously the scale/impact can be smaller). It’s clear that forecasting is distinct from finding a causal link, but the general process of incorporating something in decision making process and having that something affect decisions is similar.
Also, I am very skeptical about the value of research.
As a (sorta) constructive proposal. Consider how foresight (as a formerly great practice from the 1960s) is used. Consider that foresight (essentially a more advanced form of working with the future than extrapolation and than forecasting) is not promoted or supported by EA-related organizations (or supported to a very limited degree). This is an example of how funding can and should be shifted.
I think it’s clear from Marcus experience and his argument that forecasting per se failed to produce results. There are many adjacent areas that are related to better epistemology and decision making and the money should be rationally diverted there.
As an applied futurologist specializing in human augmentation and management of innovation I can “push my favorite solutions” here, but my position is more general—find better target for this funding.
This obviously assumes Marcus has a sufficient level of experience to justify the claims. Which I think, given other comments, can be adequately challenged.
It would be good to know what metric/threshold/examples would be taken as forecasting delivering adequate impact to justify funding. From examples in this thread alone, we can see senior government decision makers in both the U.K. (including Ministerial teams and critical committees) and US, frontier labs safety teams, and philanthropic funds moving tens of millions of dollars a year) have utilised forecasting (either the process or the outputs) to inform their decisions.
The argument of it only shifting a decision 1-2% is totally fair. But to keep consistent I’d expect the same people who make that argument to also be highly sceptical of the vast majority of research funding.
Marcus’s experience can be questioned and his position challenged, but in other comments other knowledgeable and experienced people supported some of his arguments, even though they were objecting to others. So I would say that in general Marcus’s position is strong. It’s clear it’s provocative, but I don’t see a problem with that personally.
The metrics could be chosen based on your overall decision making system. The end points are measured by NPV, QLY, etc. It’s clear that you need some intermediate metrics, of course, which I would say, is the number and scale of decisions where forecasting not only “informed” the decisions, but “determined”.
Examples from determining the impact of scientific research: lead is bad ⇒ leaded gasoline banned. CO2 is bad ⇒ fight global warming.
I would ideally like to see something similar (obviously the scale/impact can be smaller). It’s clear that forecasting is distinct from finding a causal link, but the general process of incorporating something in decision making process and having that something affect decisions is similar.
Also, I am very skeptical about the value of research.
As a (sorta) constructive proposal. Consider how foresight (as a formerly great practice from the 1960s) is used. Consider that foresight (essentially a more advanced form of working with the future than extrapolation and than forecasting) is not promoted or supported by EA-related organizations (or supported to a very limited degree). This is an example of how funding can and should be shifted.