  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.

Computer Science and Game Theory;Systems and Control