In such cases, perhaps the rules would be to pick a probability based on the resolution of past games—with the teams tied, it resolves at 50%, and with one team up by 3 runs in the 7th inning, it resolves at whatever percentage of games where a team is up by 3 runs at that point in the game wins.
Sounds like Pascal’s problem of the points, where the solution is to provide the expected value of winnings, and not merely allocate all winnings to which player has the highest probability of victory. Suppose 1 team has 51% probability of winning—should the traders who bought that always get a 100% payoff and the 49% shares be worthless? That sounds extremely distortionary if it happens at all frequently.
Plus quite hard to estimate: if you had a model more accurate than the prediction market, it’s not clear why you would be using the PM in the first place. On the other hand, there is a source of the expected value of each share which incorporates all available information and is indeed close at hand: the share prices themselves. Seems much fairer to simply liquidate the market and assign everyone the last traded value of their share.
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.)
Sounds like Pascal’s problem of the points, where the solution is to provide the expected value of winnings, and not merely allocate all winnings to which player has the highest probability of victory. Suppose 1 team has 51% probability of winning—should the traders who bought that always get a 100% payoff and the 49% shares be worthless? That sounds extremely distortionary if it happens at all frequently.
Plus quite hard to estimate: if you had a model more accurate than the prediction market, it’s not clear why you would be using the PM in the first place. On the other hand, there is a source of the expected value of each share which incorporates all available information and is indeed close at hand: the share prices themselves. Seems much fairer to simply liquidate the market and assign everyone the last traded value of their share.
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.)