Thread by @VitalikButerin

There's a big difference between statistical models and prediction markets this US election; and it's a puzzle why this is happening.

Some guesses:

1. Bets on prediction markets correctly incorporate the possibility of heightened election meddling, voter suppression, etc affecting the outcome, but statistical models just assume the voting process is fair

(This is the pro-prediction-market explanation)

2. Prediction markets are difficult to access for statistical/politics experts, they're too small for hedge funds to hire those experts, and the people (esp wealthy people) with the most access to PMs are more optimistic about Trump

(This is the pro-stats-model explanation)

Explanation 3, that "the experts" are incorrigibly dumb and just haven't learned their lessons around detecting surprise pro-Trump voters as happened in 2016, intuitively just feels unlikely to me. Far too many people have talked about this issue.

Note that PMs are way easier to access this year (though maybe not enough people know it?):

catnip.exchange

But seriously, any ideas? @robertwiblin @technocrypto @ESYudkowsky @robinhanson

@NateSilver538 how do your models account for voter suppression?