What is the Moonbird algorithm? Why do you call it ML? because algorithm won’t tell me much without the data used to train it?
The algorithm that first springs to mind is to treat every number of predictions separately and apply kNN (in logistic space?). Better: if there are N predictions, average over the kNN applied to every one of the 2^N subsets of the predictions. Maybe weight by how well trained the different lengths are.
Tetlock tells us that although individuals are overconfident, crowds are underconfident, so once we’ve averaged, we should shift away from 0.5. This helps a bit in this case, but Moonbird does a lot better. I guess it’s increasing confidence when the crowd is agreed and decreasing confidence when the crowd disagrees.
lalaithion and jbeshir made predictions at the time of the announcement, while bendini and I made predictions at closing time, about two months later. This should have been a substantial advantage for us. Indeed, the fall in bitcoin from $9k to $6k helped us a lot. On many questions about whether something occurs (eg, Fatah and Hamas reconcile) we should multiply the probability by about 6⁄8 because we were considering a 6 month span, while they were considering an 8 month span. But I was systematically less confident than they were and I think bendini only about as confident.