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We consider a framework involving behavioral economics and machine learning.Rationally inattentive Bayesian agents make decisions based on their posteriordistribution, utility function and information acquisition cost Renyidivergence which generalizes Shannon mutual information). By observing thesedecisions, how can an observer estimate the utility function and informationacquisition cost? Using deep learning, we estimate framing information(essential extrinsic features) that determines the agent's attention strategy.Then we present a preference based inverse reinforcement learning algorithm totest for rational inattention: is the agent an utility maximizer, attentionmaximizer, and does an information cost function exist that rationalizes thedata? The test imposes a Renyi mutual information constraint which impacts howthe agent can select attention strategies to maximize their expected utility.The test provides constructive estimates of the utility function andinformation acquisition cost of the agent. We illustrate these methods on amassive YouTube dataset for characterizing the commenting behavior of users.

Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics