I think one idea I'm excited about is the idea that predictions can be made of prediction accuracy. This seems pretty useful to me.
Say there's a forecaster Sophia who's making a bunch of predictions for pay. She uses her predictions to make a meta-prediction of her total prediction-score on a log-loss scoring function (on all predictions except her meta-predictions). She says that she's 90% sure that her total loss score will be between -5 and -12.
The problem is that you probably don't think you can trust Sophia unless she has a lot of experience
One nice thing about adjustments is that they can be applied to many forecasts. Like, I can estimate the adjustment for someone's [list of 500 forecasts] without having to look at each one.
Over time, I assume that there would be heuristics for adjustments, like, "Oh, people of this reference class typically get a +20% adjustment", similar to margins of error in engineering.
That said, these are my assumptions, I'm not sure what forecasters will find to be the best in practice.