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In this paper we study a rational inattention model in environments where thedecision maker faces uncertainty about the true prior distribution over states.The decision maker seeks to select a stochastic choice rule over a finite setof alternatives that is robust to prior ambiguity. We fully characterize thedistributional robustness of the rational inattention model in terms of atractable concave program. We establish necessary and sufficient conditions toconstruct robust consideration sets. Finally, we quantify the impact of prioruncertainty, by introducing the notion of \emph{Worst-Case Sensitivity}.

On the Distributional Robustness of Finite Rational Inattention Models