# Learning Approximately Optimal Contracts

arXiv: https://arxiv.org/abs/1811.06736 · HTML: https://mlsanity.com/paper/1811.06736/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Contracting with a Learning Agent](https://mlsanity.com/paper/2401.16198/) (2024)
- [Incentivized Learning in Principal-Agent Bandit Games](https://mlsanity.com/paper/2403.03811/) (2024)
- [Deep Contract Design via Discontinuous Networks](https://mlsanity.com/paper/2307.02318/) (2023)
- [Delegated Classification](https://mlsanity.com/paper/2306.11475/) (2023)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [Learning Optimal Contracts: How to Exploit Small Action Spaces](https://mlsanity.com/paper/2309.09801/) (2023)
- [Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability](https://mlsanity.com/paper/2402.17108/) (2024)
- [Learning in Bayesian Stackelberg Games With Unknown Follower's Types](https://mlsanity.com/paper/2602.00771/) (2026)
- [Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition](https://mlsanity.com/paper/2505.17379/) (2025)
- [The Optimal Sample Complexity of Linear Contracts](https://mlsanity.com/paper/2601.01496/) (2026)
- [Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts](https://mlsanity.com/paper/2609.20353/) (2026)

## Abstract (arXiv)

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space: she sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal to obtain a contract that is within eps of her optimal net profit for every eps>0. Our results are robust even when considering risk-averse agents. Furthermore, we show that when there are only two possible outcomes or the agent is risk-neutral, the algorithm's outcome approximates the optimal contract described in the classical theory.