Fair Team Contracts

2025-12-22

AI Summary

This paper studies the principal’s problem of designing revenue-maximizing fair contracts for a team of collaborative agents, ensuring both incentive compatibility—i.e., motivating costly effort—and minimum-share fairness constraints. We propose, for the first time, a linear contract structure and develop an FPTAS for additive success functions and a constant-factor approximation algorithm for submodular success functions, jointly optimizing fairness and incentive compatibility. Theoretically, our fair contracts increase the principal’s revenue by up to 25% compared to the optimal non-discriminatory (unfair) contract, while remaining computable in polynomial time. Our key innovation lies in embedding minimum-share fairness directly into the linear contract framework and establishing a novel cross-methodological analysis that bridges submodular optimization and mechanism design.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Non-convex Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

A principal selects a team of agents for collaborating on a joint project. The principal aims to design a revenue-optimal contract that incentivize the team of agents to exert costly effort while satisfying fairness constraints. We show that the optimal fair contract ensures that there is a minimum share, and every agent receives a linear contract weakly higher than the minimum share that is sufficient to incentivize them to exert costly effort. We utilize this structure to design an FPTAS for additive success functions and a constant approximation algorithm for submodular success functions. Moreover, we show that adopting optimal fair contracts can lead to a 25% revenue increase compared to the optimal non-discriminatory contracts even for additive success functions.

Problem

• Designing revenue-optimal contracts for team collaboration under fairness constraints.
• Ensuring minimum share and linear contracts to incentivize costly agent effort.
• Comparing fair contract revenue with non-discriminatory contracts for additive functions.

Innovation

• Fair contracts ensure minimum share for agents
• Linear contracts incentivize costly effort efficiently
• FPTAS and approximation algorithms for success functions