Perhaps surprisingly, statistics has an answer, and that answer is no. If in your application the usefulness of a statistical model is equivalent to its predictive performance, then choose your model using cross-validation, which directly optimizes for predictive performance. When that gets too expensive, use the AIC, which is equivalent to cross-validation as the amount of data grows without bound. But if the true model is available, neither AIC nor cross-validation will pick it out of the set of models being considered as the amount of data grows without bound.