Title

Robust Predictions in Games with Incomplete Information

Author(s)

Dirk Bergemann Dirk Bergemann (Yale University)

Stephen Morris Stephen Morris (Princeton University)

Abstract

We analyze games of incomplete information and offer equilibrium predictions which are valid for, and in this sense robust to, all possible private information structures that the agents may have. We completely characterize the set of Bayes correlated equilibria in a class of games with quadratic payoffs and normally distributed uncertainty in terms of restrictions on the first and second moments of the equilibrium action-state distribution. We derive exact bounds on how prior knowledge about the private information refines the set of equilibrium predictions. We consider information sharing among firms under demand uncertainty and find newly optimal information policies via the Bayes correlated equilibria. Finally, we reverse the perspective and investigate the identification problem under concerns for robustness to private information. The presence of private information leads to set rather than point identification of the structural parameters of the game.

Creation Date

2012-09

Section URL ID

ET

Paper Number

023-2011

URL

https://detc.princeton.edu/wp-content/uploads/2016/11/wp023_2011-revised.pdf

File Function

Jel

C72, C73, D43, D83

Keyword(s)

Incomplete information, Correlated equilibrium, Robustness to private information, Moments restrictions, Identification, Informations bounds

Suppress

false

Series

10