With meta-modeling you can build an ensemble (single model) that beats any individual model. This should also have less variance (See Breiman's Bagging Predicto

With meta-modeling you can build an ensemble (single model) that beats any individual model. This should also have less variance (See Breiman's Bagging Predictors). I'm saying that even if there is a super talent on Numerai, one should still use, say, 0.95 to 0.05 weights, to "hedge your bets" and improve accuracy. With enough competitors, it becomes near impossible for a single agent to beat all the others combined. Kaggle competitions also see this. https://www.boozallen.com/content/dam/boozallen/documents/2015/12/2015-FIeld-Guide-To-Data-Science.pdf https://www.boozallen.com/content/dam/boozallen/documents/20... [page 93] shows a graph where a simple average of the top models in a competition gives an ensemble model with higher accuracy than any of the individual competitors. I think that is largely the beauty of Numerai. Using adversarial agents to build a "collaborative" model. Disclaimer: Won a few bitcoin on Numerai.