I think this is a challenging and non-trivial question, which I’ve considered before, but I’m less pessimistic than some other commenters.
I think what we really should do is to fund someone to research and build a rigorous training set along these lines, using some kind of bias avoiding methodology (eg clever pre-registration, systematic protocols for what data to include, etc ).
I find it conceivable but very implausible that doing this will make you worse, and can certainly imagine that doing it might make you a lot better. Though most plausibly it will have a small positive effect (though that might entirely be due to the benefits of just doing deliberate practice in thinking at all).
Also Tegan McCaslin did some work on this and at one point we ran a test workshop with some superforecasters trying to predict decades of steamship development in the 19ty century based on a dataset she’d made. Could did that out for you.
I think this is a challenging and non-trivial question, which I’ve considered before, but I’m less pessimistic than some other commenters.
I think what we really should do is to fund someone to research and build a rigorous training set along these lines, using some kind of bias avoiding methodology (eg clever pre-registration, systematic protocols for what data to include, etc ).
I find it conceivable but very implausible that doing this will make you worse, and can certainly imagine that doing it might make you a lot better. Though most plausibly it will have a small positive effect (though that might entirely be due to the benefits of just doing deliberate practice in thinking at all).
Also Tegan McCaslin did some work on this and at one point we ran a test workshop with some superforecasters trying to predict decades of steamship development in the 19ty century based on a dataset she’d made. Could did that out for you.