Fair Contracts in Principal-Agent Games with Heterogeneous Types

2025-06-18

AI Summary

In multi-agent principal–agent delegation games, implicit heterogeneity in agents’ latent capabilities leads to unfair wealth distribution. Method: We formulate a repeated Stackelberg game framework and propose a fairness-aware design method based on adaptive linear contracts. Contribution/Results: We theoretically establish that—without explicit agent type identification—homogeneous linear contracts suffice to guarantee cross-agent outcome fairness in dynamic interactions, while preserving both system efficiency and stability. Our approach integrates reinforcement learning–driven contract policy optimization, linear parametric modeling, sequential social-dilemma experiments, and joint fairness–efficiency evaluation. Empirical results across diverse heterogeneous settings demonstrate a 42% reduction in the Gini coefficient, a 27% increase in group cooperation rate, and maintenance of 98.5% of baseline aggregate utility.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Achieving fairness in multi-agent systems with heterogeneous types
• Addressing unequal wealth distribution under same rules
• Balancing fairness and efficiency in principal-agent contracts

Innovation

• Repeated principal-agent games framework
• Homogeneous linear contracts for fairness
• Balances equity and system efficiency