Dynamic Mechanism and Information Design in AI-Agent Environments: A General Analytical Framework
This framework designs optimal mechanisms for AI agents dealing with evolving hidden information and unobservable actions under partial verification. It establishes a dynamic revelation principle, showing how truthful reporting and subsequent obedience can be linked through continuation incentives. The result is a system that quantifies transaction costs related to information, verification, and enforcing hidden actions in complex multi-agent settings.