Thanks for your reply. The possibility of asymmetry suggests even more that we shouldnât predict in the whole [0%-100%] range, but rather stick to whatever half of the interval we feel more comfortable with. All we have to do is to get in the habit of flipping the âsignâ of the question (i.e, taking the complement of the sample space) when needed, which usually amounts to adding the phrase âItâs not the case thatâ in front of the prediction. This leads to roughly double the number of samples per bin, and therefore more precise estimates of our calibration. And since we have to map an event to a set that is now half the size it was before, it seems easier for us to get better at it over time.
Do you see any reason not to change Open Philanthropyâs approach to forecasting besides the immense logistic effort this implies?
Thanks for your reply. The possibility of asymmetry suggests even more that we shouldnât predict in the whole [0%-100%] range, but rather stick to whatever half of the interval we feel more comfortable with. All we have to do is to get in the habit of flipping the âsignâ of the question (i.e, taking the complement of the sample space) when needed, which usually amounts to adding the phrase âItâs not the case thatâ in front of the prediction. This leads to roughly double the number of samples per bin, and therefore more precise estimates of our calibration. And since we have to map an event to a set that is now half the size it was before, it seems easier for us to get better at it over time.
Do you see any reason not to change Open Philanthropyâs approach to forecasting besides the immense logistic effort this implies?