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.

翻译：我们研究了委托-代理问题中合同设计的基础性不确定性问题。先前研究主要关注贝叶斯设置，其中委托人的不确定性通过代理人类型的概率分布建模。本文研究委托人缺乏代理人类型分布信息的场景。特别地，在该场景中，委托人仅知道定义代理人可能行动成本的不确定集合。因此，委托人采用稳健（对抗性）方法，尝试设计最小化（加性）遗憾的合同：即委托人若知晓代理人成本时可能获得的收益与实际所选合同下获得的收益之间的最大差值。