Uncertainty and symmetry in the US elections

Nate Silver’s predictions at 538 give Donald Trump a much higher chance of winning the election than anyone else’s: at the time of writing, 20% vs 8% from the Upshot, 5% from Daily Kos, or 1% from Sam Wang at the Princeton Election Consortium.

That’s mostly not because Nate Silver thinks Trump is doing much better: 538 estimates 326 Electoral College votes for Clinton; Daily Kos has 334; the Princeton folks have 335.  The popular vote margin is estimated as 5.7% by 538 and about 8.4% by Princeton (their ‘meta-margin’ is 4.2%).

Everyone also pretty much agrees that the uncertainty in the votes is symmetric: if the polls are wrong, the estimated support for Clinton could as easily be too high as too low.  But that’s the uncertainty in the margin, not in the chance of winning.  Probabilities can’t go above 100% or below 0%, and when they get close to these limits, a symmetric uncertainty in the vote margin has to turn into an asymmetric uncertainty in the probability prediction, and a larger uncertainty has to pull the probability further away from the boundaries.

Nate Silver’s model thinks that opinion polls can be off by 6 or 7 percent in either direction even this close to the elections; the others don’t. It’s question that history can’t definitively answer, because there isn’t enough  history to work with. If Silver is wrong, we won’t know even after the election; even if he’s right, the most likely outcome is for the results to look pretty much like everyone predicts.