Anthony DiGiovanni 🔸 comments on Forecasting is Way Overrated, and We Should Stop Funding It

[Anthony DiGiovanni 🔸]

When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems. You solve problems by starting with the problem, and then you see which tools are useful for solving it.

I definitely have my own gripes about EA/​rationalist attitudes towards forecasting (see here), but maybe your objection is a level confusion:

1. I think when people talk about “leveraging forecasting into better decisions”, they’re saying: “‘Better’ decisions just are decisions guided by the normatively correct beliefs. Namely, they’re decisions that make reasonable-seeming tradeoffs between possible outcomes given the normatively correct beliefs about the plausibility of those outcomes. So our decisions will be more aligned with this standard of ‘better’ if our beliefs are formed by deferring to well-calibrated forecasts.”

   1. E.g. they’re saying, “When navigating AI risk, we’ll make decisions that we endorse more if those decisions are guided by the credences of folks who’ve been unusually successful at forecasting AI developments.”

   2. (At least, that’s the steelman. Maybe I’m being too charitable!)

2. Whereas you seem to be asking something like: “We already know which beliefs are reasonable. Do these beliefs tell us that ‘plug forecasts into some decision-making procedure’ seems likely to lead to good outcomes (i.e., that this is a ‘useful tool’)?”

[Anthony DiGiovanni 🔸]

(My gripes discussed in the linked post above, FWIW: Re: (1), the typical EA operationalization of “well-calibrated”, and judgments about how to defer to people based on their calibration on some reference class of past questions, are based on very questionable epistemological assumptions. See also this great post.)