If you are asking if we could effectively use some transformation on their results to get a useful signal, my strong net is “maybe, but barely so.” I know there are cases in finance where poor predictors are actually systematically wrong, in ways that a good predictor could use for updating; but expect that’s for specific reasons we don’t have.
My interpretation: there’s no such thing as negative value of information. If the mean of the crowdworkers’ estimates were reliably in the wrong direction (compared with Elizabeth’s prior) then that would allow you to update Elizabeth’s prior to make it more accurate.
Would they be useful for finding the wrong answer?
If you are asking if we could effectively use some transformation on their results to get a useful signal, my strong net is “maybe, but barely so.” I know there are cases in finance where poor predictors are actually systematically wrong, in ways that a good predictor could use for updating; but expect that’s for specific reasons we don’t have.
I’m afraid I don’t understand your question, could you clarify?
My interpretation: there’s no such thing as negative value of information. If the mean of the crowdworkers’ estimates were reliably in the wrong direction (compared with Elizabeth’s prior) then that would allow you to update Elizabeth’s prior to make it more accurate.
An oracle that is always wrong can still be useful.