# Regret-Minimizing Contracts: Agency Under Uncertainty

arXiv: https://arxiv.org/abs/2402.13156 · HTML: https://mlsanity.com/paper/2402.13156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Contracting With a Reinforcement Learning Agent by Playing Trick or Treat](https://mlsanity.com/paper/2410.13520/) (2024)

## Abstract (arXiv)

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.