# Generalized Principal-Agency: Contracts, Information, Games and Beyond

arXiv: https://arxiv.org/abs/2209.01146 · HTML: https://mlsanity.com/paper/2209.01146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective](https://mlsanity.com/paper/2504.03255/) (2025)
- [Generalized Principal-Agent Problem with a Learning Agent](https://mlsanity.com/paper/2402.09721/) (2024)
- [Robust Reward Design for Markov Decision Processes](https://mlsanity.com/paper/2406.05086/) (2024)
- [Contracting With a Reinforcement Learning Agent by Playing Trick or Treat](https://mlsanity.com/paper/2410.13520/) (2024)

## Abstract (arXiv)

In the principal-agent problem formulated by Myerson'82, agents have private information (type) and make private decisions (action), both of which are unobservable to the principal. Myerson pointed out an elegant linear programming solution that relies on the revelation principle. This paper extends Myerson's results to a more general setting where the principal's action space can be infinite and subject to additional design constraints. Our generalized principal-agent model unifies several important design problems including contract design, information design, and Bayesian Stackelberg games, and encompasses them as special cases. We first extend the revelation principle to this general model, based on which a polynomial-time algorithm is then derived for computing the optimal mechanism for the principal. This algorithm not only implies new efficient solutions simultaneously for all the aforementioned special cases but also significantly simplifies previously known algorithms designed for special cases. Inspired by the recent interest in the algorithmic design of a single contract and menu of contracts, we study such constrained design problems to our general principal-agent model. In contrast to the above unification, our results here illustrate the other facet of diversity among different principal-agent design problems and demonstrate how their different structures can lead to different complexities: some are tractable whereas others are APX-hard. Finally, we reveal an interesting connection of our model to the problem of information acquisition for decision making and study its algorithmic properties in general.