Yamashita, Takuro and Smolin, Alexey (2022) Information Design in Concave Games. TSE Working Paper, n. 22-1313, Toulouse, France

Warning
There is a more recent version of this item available.
[thumbnail of wp_tse_1313.pdf]
Preview
Text
Download (730kB) | Preview

Abstract

We study information design in games with a continuum of actions such that the players’ payoffs are concave in their own actions. A designer chooses an information structure–a joint distribution of a state and a private signal of each player. The information structure induces a Bayesian game and is evaluated according to the expected designer’s payoff under the equilibrium play. We develop a method that facilitates the search for an optimal information structure, i.e., one that cannot be outperformed by any other information structure, however complex. We show an information structure is optimal whenever it induces the strategies that can be implemented by an incentive contract in a dual, principal-agent problem which aggregates marginal payoffs of the players in the original game. We use this result to establish the optimality of Gaussian information structures in settings with quadratic payoffs and a multivariate normally distributed state. We analyze the details of optimal structures in a differentiated Bertrand competition and in a prediction game.

Item Type: Monograph (Working Paper)
Language: English
Date: March 2022
Place of Publication: Toulouse, France
Uncontrolled Keywords: Information design, Bayesian persuasion, Concave games, Duality, First-order approach
JEL Classification: D42 - Monopoly
D82 - Asymmetric and Private Information
D83 - Search; Learning; Information and Knowledge; Communication; Belief
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 09 Mar 2022 11:16
Last Modified: 28 Mar 2023 07:06
OAI Identifier: oai:tse-fr.eu:126692
URI: https://publications.ut-capitole.fr/id/eprint/44754

Available Versions of this Item

View Item

Downloads

Downloads per month over past year