Return
Muddled Information
DOI:10.1086/701604.png)
Abstract
En 中文
We study a model of signaling in which agents are heterogeneous on two dimensions. An agent's natural action is the action taken in the absence of signaling concerns. Her gaming ability parameterizes the cost of increasing the action. Equilibrium behavior muddles information across dimensions. As incentives to take higher actions increase-due to higher stakes or more manipulable signaling technology-more information is revealed about gaming ability, and less about natural actions. We explore a new externality: showing agents' actions to additional observers can worsen information for existing observers. Applications to credit scoring, school testing, and web searching are discussed.
Keywords:
PRIVATE INFORMATION
SIGNALS
MODEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
2.6K
Citations:
3.2W

