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7) This is a reversal from 2016, where "models" had Trump higher than prediction markets. So what's right? I mean, really, who knows. But here are some thoughts.
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9) To combat this, they: a) combine two statistical techniques chosen out of who knows how large a set b) sample 100 values of a parameter and choose the 'best' fit c) add in an arbitrary coefficient for COVID d) fit the model to 18 data points glad they aren't overfitting
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11) So between it (13%) and prediction markets (33%) what do I think? Well, they're not actually predicting quite the same thing. They might disagree in the case of a contested election--with 538 predicting the ballots cast, and prediction markets reflecting inaguration.
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12) That might make up something like 5% difference between them, but I doubt it's 13% vs 33%. And note--prediction markets get to read 538; 538 doesn't model based on FTX. Prediction markets are at 33% *after adjusting down* for 538's information. Otherwise they might be 40%.
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13) So what's really going on here? My best guess, roughly: There are known unknowns; models capture those. There are unknown unknowns; models try to model the distribution of those.
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14) And then there are intuitions -- something like "yeah, but COVID". The unknown unknown unknowns -- the model's uncertainty about how many unknown unknowns are likely to happen this year, relative to others. Those were high in 2016. They're way higher now.
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15) What are the odds that mail-in voting turns out very different than expected in an election blowing through records in mail-in voting? What are the odds there's $5b of Super PAC spending coming on November 2nd? What are the odds COVID delays the election in some states?
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16) 2012 was a pretty 'standard', boring election. Incumbent with average popularity, slightly unpopular challenger, decent economy. 2016 was weird: a candidate unlike what we'd seen before. Almost everyone was off by a bunch there--a few % of the popular vote.
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17) How much wackier is 2020 than 2016? IDK -- probably a lot more. I sort of think that volatility here should be something like 9x the baseline and 3x 2016. So, idk, I think my model would be a fair bit less certain than 538 etc.'s, in ways that aren't reflected by polling.
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18) And then, take a step back. How surprised would you be if Trump won--if it turns out polls were off by 5%, Trump lost popular vote but won Electoral college? IDK -- I wouldn't be shocked. And if you say the odds Trump wins are 10%--
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19) idk, I guess I feel like the odds all these models are just kinda busted and this is a wacky election are greater than 10%. It can be dangerous to trust intuition above numbers, and so I'm definitely fading to the models here. But 10% kind of fails my smell test.
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21) to be clear here, I think Biden is the heavy favorite -- and *very* heavy favorite for the popular vote. I think the odds Trump wins aren't that much higher than the odds he loses but tries to say he won. But I think both of those are above 10%.
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22) Both sides are doubling down, e.g. twitter.com/NateSilver538/ If is right that 's COVID strategy sucks, then Trump very likely loses. But the public secretly liking it more than they let on is an example of a possible systematic bias.
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Trump doubling down on a series of extremely unpopular messages about COVID—after having caught COVID, and at a time when COVID cases are rising again nationally—is about the worst possible closing pitch, and one has to wonder about how downballot GOP candidates feel about it.
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