Before revision:  The main results characterize which rationalizable actions and Nash equilibria can be predicted when agents observe a finite quantity of data , and how much data is needed to predict various solutions. This number of observations needed depends on the degree of strictness of the solution and speed of common learning .
After revision:  The main results characterize which rationalizable actions and Nash equilibria can be predicted when agents observe a finite quantity of data grows large, and provide bounds for small datasets. The approach generates new predictions, e.g. that speculative trade is more likely given high-dimensional data, and that coordination is less likely given noisy data .