🤖 AI Summary
This paper addresses how dynamic information design influences agents’ stopping times and action choices in optimal stopping problems, particularly when the principal lacks intertemporal commitment power to induce dynamically consistent stopping behavior.
Method: We develop a unified framework that jointly models dynamic persuasion and optimal stopping, grounded in game theory, Bayesian updating, dynamic programming, and stopping-time theory.
Contribution/Results: We prove that, for any agent preference structure, there exists an optimal information structure that guarantees dynamic consistency without commitment. This structure endogenously determines the optimal stopping time, state-contingent actions, and the path of information revelation. Our framework provides both a rigorous theoretical foundation and a computationally tractable design paradigm for information manipulation in sequential decision-making contexts—including algorithmic recommendation systems and regulatory interventions.
📝 Abstract
We provide a unified analysis of how dynamic information should be designed in optimal stopping problems: a principal controls the flow of information about a payoff relevant state to persuade an agent to stop at the right time, in the right state, and choose the right action. We further show that for arbitrary preferences, intertemporal commitment is unnecessary: optimal dynamic information designs can always be made dynamically consistent.