Fair Contracts in Principal-Agent Games with Heterogeneous Types

TL;DR

This paper introduces a framework for fairness in multi-agent principal-agent games with heterogeneous types, enabling adaptive contracts that promote equitable outcomes without sacrificing efficiency.

Contribution

It proposes a novel fairness-aware principal-agent model that learns linear contracts to equalize outcomes among diverse agents in sequential social dilemmas.

Findings

Fair contracts can be learned to promote equity among agents.

Fairness does not reduce overall system efficiency.

Adaptive contracts stabilize outcomes in heterogeneous agent settings.

Abstract

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.