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PKP Metadata Items |
Metadata for this document |
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| 1. |
Title |
Title of document |
The persuasion duality |
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| 2. |
Creator |
Author's name, affiliation |
Piotr Dworczak; Department of Economics, Northwestern University |
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| 2. |
Creator |
Author's name, affiliation |
Anton Kolotilin; School of Economics, UNSW Business School |
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Subject |
Subject(s) |
Bayesian persuasion, information design, duality theory, price function, moment persuasion, convex partition |
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Subject |
Subject classification |
D82, D83 |
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| 4. |
Description |
Abstract |
We present a unified duality approach to Bayesian persuasion. The optimal dual variable, interpreted as a price function on the state space, is shown to be a supergradient of the concave closure of the objective function at the prior belief. Strong duality holds when the objective function is Lipschitz continuous.
When the objective depends on the posterior belief through a set of moments, the price function induces prices for posterior moments that solve the corresponding dual problem. Thus, our general approach unifies known results for one-dimensional moment persuasion, while yielding new results for the multi-dimensional case. In particular, we provide a condition for the optimality of convex-partitional signals, derive structural properties of solutions, and characterize the optimal persuasion scheme when the state is two-dimensional and the objective is quadratic. |
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Publisher |
Organizing agency, location |
Econometric Society |
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Contributor |
Sponsor(s) |
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| 7. |
Date |
(YYYY-MM-DD) |
2024-11-14 |
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Type |
Status & genre |
Peer-reviewed Article |
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Type |
Type |
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| 9. |
Format |
File format |
pdf
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| 10. |
Identifier |
Universal Resource Indicator |
https://econtheory.org/ojs/index.php/te/article/view/20241701 |
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| 11. |
Source |
Journal/conference title; vol., no. (year) |
Theoretical Economics; Volume 19, Number 4 (November 2024) |
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Language |
English=en |
en |
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| 15. |
Rights |
Copyright and permissions |
Authors who publish in Theoretical Economics will release their articles under the Creative Commons Attribution-NonCommercial license. This license allows anyone to copy and distribute the article for non-commercial purposes provided that appropriate attribution is given. |