Fair Contracts in Principal-Agent Games with Heterogeneous Types
This work addresses fairness and stability issues in sequential social dilemmas for systems with heterogeneous agents, offering a novel approach to balance equity and performance.
The paper tackles the problem of achieving fairness in multi-agent systems with hidden heterogeneity by proposing a framework based on repeated principal-agent games, where a fairness-aware principal learns homogeneous linear contracts that equalize outcomes across agents without sacrificing efficiency.
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.