On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment
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Abstract (arXiv)
This paper studies dynamic mechanism design in a quasilinear Markovian environment and analyzes a direct mechanism model of a principal-agent framework in which the agent is allowed to exit at any period. We consider that the agent's private information, referred to as state, evolves over time. The agent makes decisions of whether to stop or continue and what to report at each period. The principal, on the other hand, chooses decision rules consisting of an allocation rule and a set of payment rules to maximize her ex-ante expected payoff. In order to influence the agent's stopping decision, one of the terminal payment rules is posted-price, i.e., it depends only on the realized stopping time of the agent. We define the incentive compatibility in this dynamic environment in terms of Bellman equations, which is then simplified by establishing a one-shot deviation principle. Given the optimality of the stopping rule, a sufficient condition for incentive compatibility is obtained by constructing the state-dependent payment rules in terms of a set of functions parameterized by the allocation rule. A necessary condition is derived from envelope theorem, which explicitly formulates the state-dependent payment rules in terms of allocation rules. A class of monotone environment is considered to characterize the optimal stopping by a threshold rule. The posted-price payment rules are then pinned down in terms of the allocation rule and the threshold function up to a constant. The incentive compatibility constraints restrict the design of the posted-price payment rule by a regular condition.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Sep 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021Informational Design of Dynamic Multi-Agent Systemcited by 4
- 2021On the Equilibrium Elicitation of Markov Games Through Information Designcited by 1
- 2022Bayesian Promised Persuasion: Dynamic Forward-Looking Multiagent Delegation with Informational Burningcited by 0
- 2025Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Unknown Environmentscited by 0
- 2025Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Unknown Environments23 Jun 2025
- 2022Bayesian Promised Persuasion: Dynamic Forward-Looking Multiagent Delegation with Informational Burning16 Jan 2022
- 2021Informational Design of Dynamic Multi-Agent System7 May 2021
- 2021On the Equilibrium Elicitation of Markov Games Through Information Design14 Feb 2021