Diversity Trumps Accuracy in Large Groups
In a recent paper with Scott Page, forthcoming in Management Science, we show that when combining the forecasts of large numbers of individuals, it is more important to select forecasters that are different from one another than those that are individually accurate. In fact, as the group size goes to infinity, only diversity (covariance) matters. The idea is that in large groups, even if the individuals are not that accurate, if they are diverse then their errors will cancel each other out. In small groups, this law of large numbers logic doesn’t hold, so it is more important that the forecasters are individually accurate. We think this result is increasingly relevant as organizations turn to prediction markets and crowdsourced forecasts to inform their decisions.