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View all- Caplin AMartin DMarx P(2024)Rationalizable learningEconomic Theory10.1007/s00199-024-01627-zOnline publication date: 31-Dec-2024
Posterior-Mean Separable Costs of Information Acquisition
Page 1196
Abstract
We analyze a problem of revealed preference given state-dependent stochastic choice data in which the payoff to a decision maker (DM) only depends on their beliefs about posterior means. Often, the DM must also learn about or pay attention to the state; in applied work on this subject, a convenient assumption is that the costs of such learning are linearly dependent in the distribution over posterior means:
[EQUATION]
where z0 is the prior mean, F is the distribution of posterior means z, and c : [0, 1] → ℝ. Our characterization via posterior-mean separable costs enables the use of well-known information design techniques to solve for the optimal choice of information.
We provide testable conditions to identify whether this assumption holds. The first condition, "No Improving Actions Switches," is standard in the literature of revealed preference from choices due to information, and states that the choices for the DM conditional on the DM's signal are optimal. The second condition, "No Improving Posterior-Mean Cycles," which is novel to this paper, states that the DM cannot improve their average payoff by switching around posterior means between menus whenever the information constraints are the same.
The full paper can be found at https://arxiv.org/pdf/2311.09496.
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Published In
July 2024
1340 pages
ISBN:9798400707049
DOI:10.1145/3670865
- Chair:
- Dirk Bergemann,
- Program Chairs:
- Robert Kleinberg,
- Daniela Saban
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Published: 17 December 2024
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View all- Caplin AMartin DMarx P(2024)Rationalizable learningEconomic Theory10.1007/s00199-024-01627-zOnline publication date: 31-Dec-2024
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