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Working Paper
Rational Inattention via Ignorance Equivalence
We present a novel approach to finite Rational Inattention (RI) models based on the ignorance equivalent, a fictitious action with state-dependent payoffs that effectively summarizes the optimal learning and conditional choices. The ignorance equivalent allows us to recast the RI problem as a standard expected utility maximization over an augmented choice set called the learning-proof menu, yielding new insights regarding the behavioral implications of RI, in particular as new actions are added to the menu. Our geometric approach is also well suited to numerical methods, outperforming ...
Working Paper
Geometric Methods for Finite Rational Inattention
We present a geometric approach to the finite Rational Inattention (RI) model, recasting it as a convex optimization problem with reduced dimensionality that is well-suited to numerical methods. We provide an algorithm that outperforms existing RI computation techniques in terms of both speed and accuracy. We also introduce methods to quantify the impact of numerical inaccuracy on the behavioral predictions and to produce robust predictions regarding the most frequently implemented actions.
Working Paper
Rational Inattention via Ignorance Equivalence
We introduce the concept of the ignorance equivalent to effectively summarize the payoff possibilities in a finite Rational Inattention problem. The ignorance equivalent is a unique fictitious action that is weakly preferable to all existing learning strategies and yet generates no new profitable learning opportunities when added to the menu of choices. We fully characterize the relationship between the ignorance equivalent and the optimal learning strategies. Agents with heterogeneous priors self-select their own ignorance equivalent, which gives rise to an expected-utility analogue of the ...