Scott Alexander

Here's a potential workaround I've never seen before.

Suppose you create a set of conditional prediction markets as above.

Then you create a set of secondary markets, asking bettors to predict the price of the first set of markets on the day before election day.

On the day before election day, either they'll have struck oil or they won't have.

So regardless of the oil situation, people will be factoring in only the true effect of the party's policies.

If you ask people today to predict those markets, they'll be predicting the true effect of the policies.

Giving an example with numbers on everything.

Thanks to AI for gaming this out with me.

25% chance of striking oil.

No oil world, 75% chance.

D increases GDP 5%, R increases GDP 2%.

D wins 50%, R wins 50%.

Yes oil world, 25% chance.

D increases GDP 10%, R increases GDP 7%.

Audio note, this continues for a long time with lots of calculations and percentages.

Scott writes, "...this doesn't completely solve the conditional problem.

There could be residual correlations based on hidden variables that affect the outcome of interest, in this case the election, without being known to betters even on election day eve.

A trivial example is some extraordinary event which happens at 12.01am on election day.

A more subtle example goes something like, suppose the economy is subtly good.

Nobody has managed to aggregate the statistics and figure this out in a legible way yet, and each individual person still only has private knowledge that the economy is good for him or herself."