Principal designs contracts for agents with mistaken beliefs
Contracting with an Agent under Model Mismatch
Machine LearningComputer Science and Game Theory
Summary
Sometimes, a boss asks a worker to do a job and pays them based on the results. But if the worker misunderstands how things actually work, they might make poor choices. The authors study how a boss who knows the true situation can still create agreements that make the worker act in the boss’s best interest. Their work shows how to set up these agreements even if the worker has incorrect ideas about the environment.
What this means in practice
- •For supply chain managers: Create contracts for partners who may misunderstand market dynamics to still achieve supply goals over time.
- •For robotics teams: Design reward schemes for robots operating under inaccurate models to encourage useful long-term behaviors.
Authors
Elif Yılmaz, Jakub Tłuczek, Victor Villin, Christos Dimitrakakis
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.