Contracting with an Agent under Model Mismatch

Abstract:In the classical principal-agent setting, the principal delegates some task to the agent, who receives a payment conditioned on the outcome of the task, which in turn benefits the principal. To align their objectives, the principal designs a contract that incentivizes the agent to take actions beneficial to the principal. In practice, however, the agent may act under misspecified beliefs about the underlying environment, leading to systematically suboptimal decisions. In this work, we study this problem through a principal-agent Markov Decision Process (PAMDP), in which the principal repeatedly interacts with an agent whose transition model may differ from the true environment dynamics. The principal has access to the true transition model and designs contracts that account for the agent's belief and incentives. We characterize contract design under model mismatch and develop a framework that enables the principal to induce desirable long-term benefits even though the agent has a potentially inaccurate understanding of the environment.

Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)

arXiv:2610.23402 [cs.LG]

(or arXiv:2610.23402v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.23402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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