Muddled Information
研究了代理人在两个维度上异质时的信号传递模型，发现均衡行为会混淆信息，且提高激励会更多揭示操纵能力而非自然行动，并探讨了向更多观察者展示行动可能恶化现有观察者信息的新外部性。
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.