 Much of computational learning theory focuses on inquirers with bounded rationality, that is, agents with cognitive limitations such as a finite memory or bounded computational capacities. Many categorical norms that do not interfere with empirical success for logically omniscient agents nonetheless limit the scope of cognitively bounded agents. For example, consider the norm of consistency: Believe that a hypothesis is false as soon as the evidence is logically inconsistent with it. The consistency principle is part of both Bayesian confirmation theory and AGM belief revision. Kelly and Schulte [1995] show that consistency prevents even agents with infinitely uncomputable cognitive powers from reliably assessing certain hypotheses. The moral is that if a theory is sufficiently complex, agents who are not logically omniscient may be unable to determine immediately whether a given piece of evidence is consistent with the theory, and need to collect more data to detect the inconsistency. But the consistency principle—and a fortiori, Bayesian updating and AGM belief revision— do not acknowledge the usefulness of “wait and see more” as a scientific strategy.