As the number of models increase, the observed pattern in prediction discrepancies allows one to decide what is causing it assuming uniform uncertainty among all possible scenarios. The observed error pattern will be consistent with many different scenarios. In some scenarios, the noisy model predictions are due to the model being correct and the other models being incorrect. In other scenarios, the model is incorrect and the other models are correct. The hypothesis I want to prove is that the “mass” (the number of states) that corresponds to assuming the model is incorrect becomes larger than the one assuming it is correct.