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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
Overconfidence, Subjective Perception, and Pricing Behavior
We study the implications of overconfidence for price setting in a monopolistic competition setup with incomplete information. Our price-setters overestimate their abilities to infer aggregate shocks from private signals. The fraction of uninformed firms is endogenous; firms can obtain information by paying a fixed cost. We find two results: (1) overconfident firms are less inclined to acquire information, and (2) prices might exhibit excess volatility driven by nonfundamental noise. We explore the empirical predictions of our model for idiosyncratic price volatility.
Working Paper
Mechanics of Linear Quadratic Gaussian Rational Inattention Tracking Problems
This paper presents a general framework for constructing and solving the multivariate static linear quadratic Gaussian (LQG) rational inattention tracking problem. We interpret the nature of the solution and the implied action of the agent, and we construct representations that formalize how the agent processes data. We apply this infrastructure to the rational inattention price-setting problem, confirming the result that a conditional response to economics shocks is possible, but casting doubt on a common assumption made in the literature. We show that multiple equilibria and a social cost ...
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 ...