  We study the fundamental problem of designing contracts in principal-agent
problems under uncertainty. Previous works mostly addressed Bayesian settings
in which principal's uncertainty is modeled as a probability distribution over
agent's types. In this paper, we study a setting in which the principal has no
distributional information about agent's type. In particular, in our setting,
the principal only knows some uncertainty set defining possible agent's action
costs. Thus, the principal takes a robust (adversarial) approach by trying to
design contracts which minimize the (additive) regret: the maximum difference
between what the principal could have obtained had them known agent's costs and
what they actually get under the selected contract.
