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 all possible private information structures that the agents may have. Our characterization of these robust predictions relies on an epistemic result which establishes a relationship between the set of Bayes Nash equilibria and the set of Bayes correlated equilibria. 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 information of the analyst refines the set of equilibrium distribution. As an application, we obtain new results regarding the optimal information sharing policy of firms under demand uncertainty. Finally, we reverse the perspective and investigate the identification problem under concerns for robustness to private information. We show how the presence of private information leads to partial rather than complete identification of the structural parameters of the game. As a prominent example we analyze the canonical problem of demand and supply identification.

Creation Date

2011-09

Section URL ID

ET

Paper Number

023_2011

URL

http://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 Re- strictions, Identi?cation, Information Bounds

Suppress

false

Series

10