Yes, that was exactly what I was thinking of, but 1) I didn’t remember the name, and 2) I wanted a concrete example relevant to prediction markets.
And I agree it’s hard to estimate in general, but the problem can still be relevant in many cases—which is why I used my example. In the baseball game, if the market closes before the game begins—we don’t have a model as good as the market, but once the game is 7/9th complete, we can do better than the pre-game market prediction.
For betting markets, the market maker may need to manage the odds differently, and for prediction markets, it’s because otherwise you’re paying people in lower brier scores for watching the games, rather than being good predictors beforehand. (The way that time-weighted brier scores work is tricky—you could get it right, but in practice it seems that last minute failures to update are fairly heavily penalized.)
Yes, that was exactly what I was thinking of, but 1) I didn’t remember the name, and 2) I wanted a concrete example relevant to prediction markets.
And I agree it’s hard to estimate in general, but the problem can still be relevant in many cases—which is why I used my example. In the baseball game, if the market closes before the game begins—we don’t have a model as good as the market, but once the game is 7/9th complete, we can do better than the pre-game market prediction.
Why close the markets, though?
For betting markets, the market maker may need to manage the odds differently, and for prediction markets, it’s because otherwise you’re paying people in lower brier scores for watching the games, rather than being good predictors beforehand. (The way that time-weighted brier scores work is tricky—you could get it right, but in practice it seems that last minute failures to update are fairly heavily penalized.)