Fair Team Contracts
Matteo Castiglioni, Junjie Chen, Yingkai Li

TL;DR
This paper designs revenue-optimal fair contracts for teams of agents, ensuring effort incentives while satisfying fairness, and demonstrates significant revenue gains over non-discriminatory contracts.
Contribution
It introduces a structured approach to fair contract design with algorithms for additive and submodular success functions, and quantifies revenue improvements.
Findings
Optimal fair contracts include a minimum share and linear contracts above it.
Provides an FPTAS for additive success functions and a constant approximation for submodular functions.
Adopting fair contracts can increase revenue by up to 25% over non-discriminatory contracts.
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.
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Taxonomy
TopicsGame Theory and Voting Systems · Auction Theory and Applications · Mobile Crowdsensing and Crowdsourcing
