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I completely agree, the frequentist view is nonsensical. This is why forecasters need to be nailed down to a specific outcome (or ‘I don’t know / it’s too close to call’ but this has to be acknowledged as opting-out).
That's my main problem with Nate Silver's modelling.
There should be large error bars around the prediction that slowly close in as the predicted event approaches.
It shouldn't be "X% Trump, Y% Kamala," it should be "X% Trump, Y% Kamala, Z% irreducible uncertainty."
The logic is "if the election were held today then here's the probability." But... the elections won't be held today. That's the whole point of the prediction for a future event, and I think it behooves them to acknowledge that uncertainty is inherent to the modelling process.
If they'd included that back when it was Trump vs. Biden, the conserved probability would have accounted for Biden suddenly dropping out and wouldn't have broken the model instantly. Also helps reflect the chance that one of the candidates dies... which also almost happened.
And if Nate trusts his model, there's a ton of money to be made in the prediction markets.
There's not a ton of money to be made if you believe the odds are 50/50. Prediction markets give Trump 60/40 odds, while Nate's model gives 50/50 odds. If your bankroll is $1M, then it's only rational to bet 167k, for an expected value of 40k. Not nothing, but not a ton of money either.
That also ignores other costs, like counterparty risk. Nate also has to deal with reputational risk: people might value his published models less if they thought he was making bets on markets that were influenced by his models. Since that's his main source of actual income, a bet would be substantially negative EV for him.
There's reputational risk for having his model diverge too far from the prediction market's call, if the markets end up looking more accurate.
And I've seen him offer various bets before.
I like Nate generally, but I end up with the feeling that the Presidential Election model is a bit too gimmicky for my tastes. As stated, he should display some factor that accounts for the inherent uncertainty of a long-term prediction, rather than making confident-seeming prognostications which get aggressively revised as new information comes in.
He's not calling his shot well in advance, he's just adjusting to the same information everyone else gets as it comes in. Credit for the model being reasonable, but what new information is it giving us?
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