# Fair Contracts in Principal-Agent Games with Heterogeneous Types

arXiv: https://arxiv.org/abs/2506.15887 · HTML: https://mlsanity.com/paper/2506.15887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Learning to Incentivize Other Learning Agents](https://mlsanity.com/paper/2006.06051/) (2020)
- [Learning Fairness in Multi-Agent Systems](https://mlsanity.com/paper/1910.14472/) (2019)
- [Simple versus Optimal Contracts](https://mlsanity.com/paper/1808.03713/) (2018)
- [Contracts under Moral Hazard and Adverse Selection](https://mlsanity.com/paper/2010.06742/) (2020)
- [Incentivized Learning in Principal-Agent Bandit Games](https://mlsanity.com/paper/2403.03811/) (2024)
- [Contracts with Private Cost per Unit-of-Effort](https://mlsanity.com/paper/2111.09179/) (2021)
- [Adaptive Mechanism Design: Learning to Promote Cooperation](https://mlsanity.com/paper/1806.04067/) (2018)
- [Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning](https://mlsanity.com/paper/2306.00324/) (2023)
- [Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents](https://mlsanity.com/paper/2304.07407/) (2023)
- [Multi-Agent Contract Design: How to Commission Multiple Agents with Individual Outcome](https://mlsanity.com/paper/2301.13654/) (2023)
- [Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards](https://mlsanity.com/paper/2308.06717/) (2023)
- [AI-driven Prices for Externalities and Sustainability in Production Markets](https://mlsanity.com/paper/2106.06060/) (2021)

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

- [Microeconomic Foundations of Multi-Agent Learning](https://mlsanity.com/paper/2601.03451/) (2026)

## Abstract (arXiv)

Fairness is desirable yet challenging to achieve within multi-agent systems, especially when agents differ in latent traits that affect their abilities. This hidden heterogeneity often leads to unequal distributions of wealth, even when agents operate under the same rules. Motivated by real-world examples, we propose a framework based on repeated principal-agent games, where a principal, who also can be seen as a player of the game, learns to offer adaptive contracts to agents. By leveraging a simple yet powerful contract structure, we show that a fairness-aware principal can learn homogeneous linear contracts that equalize outcomes across agents in a sequential social dilemma. Importantly, this fairness does not come at the cost of efficiency: our results demonstrate that it is possible to promote equity and stability in the system while preserving overall performance.