A way to frame this question is how do we get the best predictions per least amount of effort, with different strategies having different levels of effort/accuracy of output. A strategy would be considered dominated if a different strategy required both less effort and gave better accuracy. I think a pretty good case can be made for “teams of forecasters working together with their results extremized” cleary requiring less effort and being possibly more accurate or in the same ballpark as prediction markets. If that is the case, I think the argument for setting up/using prediction markets is greatly weakened. It seems like if someone did systematic research into the highest value/least resource consumption predictions, prediction markets would not score at the top of many overall rankings given its high cost. Also some evidence about the high resource cost might be that EAs, although quite excited, driven and intelligent, cannot get a prediction market going with more than a few bets on a given question.
A way to frame this question is how do we get the best predictions per least amount of effort, with different strategies having different levels of effort/accuracy of output. A strategy would be considered dominated if a different strategy required both less effort and gave better accuracy. I think a pretty good case can be made for “teams of forecasters working together with their results extremized” cleary requiring less effort and being possibly more accurate or in the same ballpark as prediction markets. If that is the case, I think the argument for setting up/using prediction markets is greatly weakened. It seems like if someone did systematic research into the highest value/least resource consumption predictions, prediction markets would not score at the top of many overall rankings given its high cost. Also some evidence about the high resource cost might be that EAs, although quite excited, driven and intelligent, cannot get a prediction market going with more than a few bets on a given question.