A response from @politicalmath, based on Smallpox: The Death of a Disease by DA Henderson:
1) There were no phases, it was just “show up, get poked”
2) There were plenty of vaccines to go around. Countries typically had millions of smallpox vaccine doses ready to go just in case
3) with no lockdowns, they could go to schools / churches / offices & line people up
4) the smallpox vaccine was incredibly heat-stable. There were batches that were still efficacious after being stored for a year at 113F
5) the public health infrastructure had a lot of practice with mass vaccinations (they did them all the time in other countries)
And it did cause a city-wide panic. Smallpox has like a 20% fatality rate so people were pretty motivated to get the vaccine. All this is from this book
https://www.amazon.com/Smallpox-Disease-Inside-Eradicating-Worldwide-ebook/dp/B002FU5O54/
Causal prediction markets.
Prediction markets (and prediction tournaments more generally) may be useful for telling us not only what will happen, but which actions will achieve our goals. One proposal for getting prediction markets to help with this is to get users to make conditional predictions. For example, we can ask the question “if Biden wins the election, GDP will be higher than if Trump wins” and use that as evidence about who to elect, and so on. But conditional predictions only predict the effect of an action if the event (e.g. who is elected) is unconfounded with the outcome (GDP). It may be that higher GDP and Biden being elected have a common cause, even if electing Biden does not increase GDP directly. One way to address this would be to have the market only pay out if Biden barely wins, or Trump barely wins, so that the confounders can be assumed to be in a similar state. Another strategy for identifying the causal effect would be to randomise. We can’t randomise the election result, but we can randomise other quantities. For instance, “we generate a number from 1-100, and audit company X if we generate 1. If we generate the number 1, how much tax evasion will we find?”. In general, in order to design action-guiding prediction markets, it may be important to draw on identification strategies from the causal inference literature.
I haven’t yet checked for existing literature on this topic. Does anyone know of any?