Rational Inattention, Optimal Consideration Sets, and Stochastic Choice

Caplin, Andrew; Dean, Mark; Leahy, John

doi:10.1093/restud/rdy037

Cited by 179 publications

(174 citation statements)

References 44 publications

(36 reference statements)

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“…Then the rational inattention model implies the formation of a consideration set, comprising those options that have strictly positive probability of being chosen (cf. Caplin, Dean, and Leahy (2016)).…”

Section: Shannon Entropy and Multinomial Logitmentioning

confidence: 99%

Discrete Choice and Rational Inattention: A General Equivalence Result

et al. 2016

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This paper establishes a general equivalence between discrete choice and rational inattention models. Matejka and McKay (2015, AER) showed that when information costs are modelled using the Shannon entropy function, the resulting choice probabilities in the rational inattention model take the multinomial logit form. By exploiting convex-analytic properties of the discrete choice model, we show that when information costs are modelled using a class of generalized entropy functions, the choice probabilities in any rational inattention model are observationally equivalent to some additive random utility discrete choice model and vice versa. Thus any additive random utility model can be given an interpretation in terms of boundedly rational behavior. This includes empirically relevant specifications such as the probit and nested logit models

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Section: Shannon Entropy and Multinomial Logitmentioning

confidence: 99%

Discrete Choice and Rational Inattention: A General Equivalence Result

et al. 2016

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“…The PDE described by equation 12is "degenerate elliptic" and hence will not in general have a classical solution. Indeed, we do not prove that our static value function is twice differentiable everywhere, and suspect it is not at points where the "consideration set" (the set of actions with π(a) > 0, Caplin et al (2018a)) changes. In our proof, we establish that the static problem value function is convex and continuously differentiable, which is sufficient to invoke a generalized version of Ito's lemma for convex functions to verify that the static value function is the solution to the continuous time problem.…”

Section: The Equivalence Of Static and Dynamic Modelsmentioning

confidence: 74%

Information Costs and Sequential Information Sampling

Hébert¹,

Woodford²

2018

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We propose a new approach to modeling the cost of information structures in rational inattention problems, the "neighborhood-based" cost functions. These cost functions have two properties that we view as desirable: they summarize the results of a sequential evidence accumulation problem, and they capture notions of "perceptual distance." The first of these properties is connected to an extensive literature in psychology and neuroscience, and the second ensures that neighborhoodbased cost functions, unlike mutual information, make accurate predictions about behavior in perceptual experiments. We compare the implications of our neighborhood-based cost functions with those of a mutual-information cost function in a series of applications: security design, global games, modeling perceptual judgments, and a linear-quadratic-Gaussian tracking problem.

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“…LetP be the optimally chosen information strategy. Caplin et al (2016) derive a necessary and sufficient condition forP .…”

Section: Optimal Rationally Inattentive Behaviormentioning

confidence: 99%