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Conference Paper: Coarse Information Design

TitleCoarse Information Design
Authors
Issue Date19-Dec-2023
Abstract

We study an information design problem with continuous state and discrete signal space. Under convex value functions, the optimal information structure is interval-partitional and exhibits a dual expectations property: each induced signal is the conditional mean (taken under the prior density) of each interval; and each interval cutoff is the conditional mean (taken under the value function curvature) of the interval formed by neighboring signals. This property enables an examination into which part of the state space is more finely partitioned. The analysis can be extended to general value functions and adapted to study coarse mechanism design.


Persistent Identifierhttp://hdl.handle.net/10722/348137

 

DC FieldValueLanguage
dc.contributor.authorLyu, Qianjun-
dc.contributor.authorSuen, Wing-
dc.contributor.authorZhang, Yimeng-
dc.date.accessioned2024-10-05T00:30:46Z-
dc.date.available2024-10-05T00:30:46Z-
dc.date.issued2023-12-19-
dc.identifier.urihttp://hdl.handle.net/10722/348137-
dc.description.abstract<p>We study an information design problem with continuous state and discrete signal space. Under convex value functions, the optimal information structure is interval-partitional and exhibits a dual expectations property: each induced signal is the conditional mean (taken under the prior density) of each interval; and each interval cutoff is the conditional mean (taken under the value function curvature) of the interval formed by neighboring signals. This property enables an examination into which part of the state space is more finely partitioned. The analysis can be extended to general value functions and adapted to study coarse mechanism design.<br></p>-
dc.languageeng-
dc.relation.ispartofEuropean winter meeting of The Econometric Society 2023 (17/12/2023-19/12/2023, Manchester)-
dc.titleCoarse Information Design-
dc.typeConference_Paper-

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