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.
* 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.