On the potential uses and current limitations of data driven learning models

The experimental study of human adjustment to economic incentives has been deadlocked for quite some time by apparently contradictory conclusions as to which is a better theory of learning. This article attempts to shed some light on this impasse by pointing out that different learning models often have different objectives that imply different model comparison criteria. The different criteria are expected to lead to the same conclusions if the models are perfectly specified, but might lead to different conclusions when they are used to compare approximations. We discuss the potential usefulness of learning models in light of the observation that they are likely to be misspecified, and outline the type of applications appropriate for each approach.

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