I learned this lesson looking at the conditional probabilities of candidates winning given they were nominated in 2016, where the candidates with less than about 10% chance of being the nominee had conditional probabilities with noise between 0 and 100%. And this was on the thickly traded real-money markets of Betfair! I personally engage in, and also recommend, just kinda throwing out any conditional probabilities that look like this, unless you have some reason to believe it’s not just noise.
Another place this causes problems is in the infinitely-useful-if-they-could-possibly-work decision markets, where you want to be able to evaluate counterfactual decisions, except these are counterfactuals so you don’t make the decision so there’s no liquidity and it can take any value.
I learned this lesson looking at the conditional probabilities of candidates winning given they were nominated in 2016, where the candidates with less than about 10% chance of being the nominee had conditional probabilities with noise between 0 and 100%. And this was on the thickly traded real-money markets of Betfair! I personally engage in, and also recommend, just kinda throwing out any conditional probabilities that look like this, unless you have some reason to believe it’s not just noise.
Another place this causes problems is in the infinitely-useful-if-they-could-possibly-work decision markets, where you want to be able to evaluate counterfactual decisions, except these are counterfactuals so you don’t make the decision so there’s no liquidity and it can take any value.