Yeah I roll to disbelieve too. One of my quantitative takeaways from Andrew Gelman’s modelling of the 2020 elections was that very few states (4/50, in particular New Hampshire, Pennsylvania, Wisconsin, and Michigan) were modelled as close enough that p(one vote changes outcome) > 1 in 10 million; New Hampshire tops the list at 1 in 8M. Optimistically assuming $100 per voter that’s still nearly a billion dollars at the very low end; a more realistic estimate would probably be ~1 OOM higher. Probably some sort of nonlinearity kicks in at this scale, or the most cost-effective tactics to sway voters cap out at relatively low levels for whatever reason?
On the flip side, I’m reminded of Scott’s essay Too much dark money in almonds? which provides an intuition pump for why it might be the case that it’s not as expensive as you may expect to swing the election:
Everyone always talks about how much money there is in politics. This is the wrong framing. The right framing is Ansolabehere et al’s: why is there so little money in politics? But Ansolabehere focuses on elections, and the mystery is wider than that. …
(in case you’re keeping track: all donations to all candidates, all lobbying, all think tanks, all advocacy organizations, the Washington Post, Vox, Mic, Mashable, Gawker, and Tumblr, combined, are still worth a little bit less than the almond industry. And Musk could buy them all.) …
In this model, the difference between politics and almonds is that if you spend $2 on almonds, you get $2 worth of almonds. In politics, if you spend $2 on Bernie Sanders, you get nothing, unless millions of other people also spend their $2 on him. People are great at spending money on direct consumption goods, and terrible at spending money on coordination problems.
(I don’t really have an opinion either way on whether more or less money should be spent on this)
I don’t think Gelman’s models are correct here. But even if they were, the numbers definitely don’t scale linearly. The vote margin ex post in the last 6 elections were all under 1M, the most recent 2 elections were under 100k. You’ll need pretty implausible assumptions (or really bad targetting) before you can fit an expectation of 10M ex ante votes to the observed ex post distribution.
Yeah I roll to disbelieve too. One of my quantitative takeaways from Andrew Gelman’s modelling of the 2020 elections was that very few states (4/50, in particular New Hampshire, Pennsylvania, Wisconsin, and Michigan) were modelled as close enough that p(one vote changes outcome) > 1 in 10 million; New Hampshire tops the list at 1 in 8M. Optimistically assuming $100 per voter that’s still nearly a billion dollars at the very low end; a more realistic estimate would probably be ~1 OOM higher. Probably some sort of nonlinearity kicks in at this scale, or the most cost-effective tactics to sway voters cap out at relatively low levels for whatever reason?
On the flip side, I’m reminded of Scott’s essay Too much dark money in almonds? which provides an intuition pump for why it might be the case that it’s not as expensive as you may expect to swing the election:
(I don’t really have an opinion either way on whether more or less money should be spent on this)
I don’t think Gelman’s models are correct here. But even if they were, the numbers definitely don’t scale linearly. The vote margin ex post in the last 6 elections were all under 1M, the most recent 2 elections were under 100k. You’ll need pretty implausible assumptions (or really bad targetting) before you can fit an expectation of 10M ex ante votes to the observed ex post distribution.