Rational inattention model under pushed information

Abstract

This paper develops a rational inattention (RI) model that separates exogenous information pushed by external platforms from travelers’ endogenous costly information processing. A platform first publishes a signal that shifts the traveler’s prior into an interim belief; the traveler then solves an RI problem conditional on this interim belief. The RI value function is convex in the interim belief, generating non-learning regions where the traveler optimally ignores additional information. Following Caplin et al. (2019); Matyskova & Montes (2023), we derive closed-form threshold beliefs that partition the belief space into learning and non-learning regimes, and show that pushed signals improve welfare only when they shift interim beliefs across these thresholds, demonstrating an information paradox. In an illustrative example with garbled signals parameterized by noise level ϵ, we obtain analytical conditions on ϵ for welfare improvement. We further show that the sensitivity of unconditional choice probabilities to ϵ varies with the information cost λ, implying that systematic variation in signal precision can screen travelers’ information costs.