Adaptive Incentive Design
This work addresses the problem of incentive design under unknown agent behavior for planners in strategic settings, offering a principled adaptive approach with theoretical guarantees.
The paper develops an algorithm for a planner to learn agents' decision-making processes and design incentives to steer their behavior toward a desired outcome, providing convergence guarantees in both noise-free and noisy settings.
We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data from strategic decision makers. The planner does not know the process by which the strategic agents make decisions. Under the assumption that the agents are utility maximizers, we model their interactions as a non-cooperative game and utilize the Nash equilibrium concept as well as myopic update rules to model the selection of their decision. By parameterizing the agents' utility functions and the incentives offered, we develop an algorithm that the planner can employ to learn the agents' decision-making processes while simultaneously designing incentives to change their response to a more desirable response from the planner's perspective. We provide convergence results for this algorithm both in the noise-free and noisy cases and present illustrative examples.