Information Design in Concave Games

Alex Smolin and Takuro Yamashita

No 17066, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Information design; Bayesian persuasion; Concave games; Duality; First-order approach (search for similar items in EconPapers)
JEL-codes: D42 D82 D83 (search for similar items in EconPapers)
Date: 2022-02
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Related works:
Working Paper: Information design in concave games (2022)
Working Paper: Information Design in Concave Games (2022)
Working Paper: Information Design in Concave Games (2022)
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