  The problem of reward design examines the interaction between a leader and a
follower, where the leader aims to shape the follower's behavior to maximize
the leader's payoff by modifying the follower's reward function. Current
approaches to reward design rely on an accurate model of how the follower
responds to reward modifications, which can be sensitive to modeling
inaccuracies. To address this issue of sensitivity, we present a solution that
offers robustness against uncertainties in modeling the follower, including 1)
how the follower breaks ties in the presence of nonunique best responses, 2)
inexact knowledge of how the follower perceives reward modifications, and 3)
bounded rationality of the follower. Our robust solution is guaranteed to exist
under mild conditions and can be obtained numerically by solving a
mixed-integer linear program. Numerical experiments on multiple test cases
demonstrate that our solution improves robustness compared to the standard
approach without incurring significant additional computing costs.
