Pulling data from prediction markets into Guesstimate is an exciting idea. A few thoughts:
* Prediction markets are usually for binary outcomes. I imagine the most useful role of binary variables in Guesstimate would be to mix two different distributions. "If Clinton wins, student debt in 2018 will look like distribution A; if Sanders wins, student debt in 2018 will look like distribution B".
* I'm not sure how Augur (or any other market) reports likelihoods, but it's good to keep in mind that market prices do NOT generally reflect any sort of average belief. See https://www.aeaweb.org/assa/2006/0106_1015_0703.pdf.
That makes sense. It would be very useful to see estimates of how well Presidential candidates would do if they got elected.
In the future, one idea would be to keep track of people's metric estimates in Guesstimate, and later score and rank them on how well they do. So if Charles always reports a 90% confidence interval that's far too optimistic, we could help adjust it automatically next time. This would also allow us to aggregate different opinions directly, essentially being like a mini prediction challenge. This would be a ways off though, and it really depends on what direction the product goes.
* Prediction markets are usually for binary outcomes. I imagine the most useful role of binary variables in Guesstimate would be to mix two different distributions. "If Clinton wins, student debt in 2018 will look like distribution A; if Sanders wins, student debt in 2018 will look like distribution B".
* I'm not sure how Augur (or any other market) reports likelihoods, but it's good to keep in mind that market prices do NOT generally reflect any sort of average belief. See https://www.aeaweb.org/assa/2006/0106_1015_0703.pdf.