Revealed Preference, Rational Inattention, and Costly Information Acquisition

By Andrew Caplin, Mark Dean. 2015

532 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Decision Sciences, Marketing

View Paper: https://www.semanticscholar.org/paper/f4a24e044dfff835a07f49e1f733d64c9a306a98

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Citations in the corpus

Monotone Stochastic Choice Models: The Case of Risk and Time Preferences (2015, background): “Another widely-used comparison involves pairs (x, y) where x is a mean-preserving spread of y.(11) Finally, experimental studies often use simple nested pairs of gambles where x = [x1, x2; p, 1 − p] and y = [y1, y2; p, 1 − p], with x1 < y1 < y2 < x2 and p ∈ (0, 1).”
Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy (2017, unknown): “The Shannon model also makes specific predictions regarding the elasticity of choice in response to incentives that fail in the experiments of Caplin and Dean (2013).”
Information Design: A Unified Perspective (2019, background): “In related work, Caplin and Dean (2015) develop a revealed preference test giving conditions under which apparent choice "mistakes" can be explained in terms of optimal costly information acquisition by the player in the presence of imperfect information.”
The Politics of Attention (2018, methodology): “First, one should not equate measured behaviors with intrinsic preferences and should instead tease them apart using the methods proposed and reviewed in Caplin and Dean (2015). Second, in light of the changing market conditions that affect the attention cost, one should not be too surprised that th”
Random Choice and Learning (2019, background): “11 Det exchang See, e.g (2017), vances in ermining the empirical content eable priors and other informatio ., Caplin and Dean (2013, 2015), Fosgerau, Melo, and Shum (2017), a this area. of the ratio n cost func Matejka and ndHêbert nal inattention m tions is an active a McKay (2015), d andWoodford…”
Attention Variation and Welfare: Theory and Evidence from a Tax Salience Experiment (2016, background): “Our estimates can thus help guide the quantitative predictions of these models.5 Third, our work contributes to the literature on boundedly rational value computation (see e.g. Gabaix, 2014; Woodford, 2012; Caplin and Dean, 2015a; Chetty, 2012).”
The Impact of Salience on Investor Behavior: Evidence from a Natural Experiment (2019, background): “Under this theory, investors optimally trade off the cost of acquiring information about the capital gain (three “mouse-clicks” in our case) against the benefit of using this information in their decision (Woodford 2012; Gabaix 2014; Caplin and Dean 2015).”
Evaluating Behaviorally Motivated Policy: Experimental Evidence from the Lightbulb Market (2015, unknown): ““Noisy” and costly thinking models, as in Gabaix (2014), Sims (2004), Caplin and Dean (2014), and others.”