This paper examines a class of signaling games with multi-dimensional private information to study how the prior, joint distribution of the private information variables affect a signal's effectiveness in revealing information about these variables. To illustrate the general problem investigated...

This paper studies information acquisition and use in network games. The network structure incorporates both strategic complements (positive links) and substitutes (negative links). An information-use game played on a correlation-adjusted network is derived. Equilibrium inefficiencies in both...

I study optimal disclosure policies in sequential contests. A contest designer chooses at which periods to publicly disclose the efforts of previous contestants. I provide results for a wide range of possible objectives for the contest designer. While different objectives involve different...

This paper proposes a tractable model of a dynamic contest where players have private information about the contest's prize. We show that private information helps to encourage players who have fallen behind, leading to an increase in aggregate incentives. We derive the optimal information...

Considered are imperfectly discriminating contests in which players may possess private information about the primitives of the game, such as the contest technology, valuations of the prize, cost functions, and budget constraints. We find general conditions under which a given contest of...

We study signaling in dynamic contests with heterogeneous players. A privately-informed challenger faces a sequence of rivals of known types. The type of future rivals determines which signal the challenger wants to produce, whereas the strategic response of current rivals determines the extent...

I study a multi-sender signaling game between an uninformed decision maker and two senders with common private information and conflicting interests. Senders can misreport information at a cost that is tied to the size of the misrepresentation. The main results concern the amount of information...