  This paper unifies two key results from economic theory, namely, revealed
rational inattention and classical revealed preference. Revealed rational
inattention tests for rationality of information acquisition for Bayesian
decision makers. On the other hand, classical revealed preference tests for
utility maximization under known budget constraints. Our first result is an
equivalence result - we unify revealed rational inattention and revealed
preference through an equivalence map over decision parameters and partial
order for payoff monotonicity over the decision space in both setups. Second,
we exploit the unification result computationally to extend robustness measures
for goodness-of-fit of revealed preference tests in the literature to revealed
rational inattention. This extension facilitates quantifying how well a
Bayesian decision maker's actions satisfy rational inattention. Finally, we
illustrate the significance of the unification result on a real-world YouTube
dataset comprising thumbnail, title and user engagement metadata from
approximately 140,000 videos. We compute the Bayesian analog of robustness
measures from revealed preference literature on YouTube metadata features
extracted from a deep auto-encoder, i.e., a deep neural network that learns
low-dimensional features of the metadata. The computed robustness values show
that YouTube user engagement fits the rational inattention model remarkably
well. All our numerical experiments are completely reproducible.
