Algorithmic Fair Contracts

Matteo Castiglioni; Junjie Chen; Yingkai Li

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arXiv · 2507.11214

Algorithmic Fair Contracts

Matteo Castiglioni·Junjie Chen·Yingkai Li

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Abstract

We initiate the algorithmic study of fair contract design. A principal assigns multiple tasks to heterogeneous agents and chooses task-level linear contracts; agents differ in costs and success probabilities, and fairness requires each agent to prefer her own task-contract bundle to any other agent's. Unlike envy-free allocations of indivisible items, envy-free full-allocation contracts always exist, but optimizing revenue under this constraint is computationally difficult: no polynomial-time algorithm can achieve any constant-factor approximation in general. We therefore identify tractable regimes. With a constant number of tasks, optimal EF, EF1, and $\epsilon$-EF contracts are computable in polynomial time. With a constant number of agents, exact EF remains hard, even for three agents, while EF1 and $\epsilon$-EF admit additive FPTAS against the EF benchmark. We also show that exact EF can have an unbounded price of fairness, whereas $\epsilon$-EF and EF1 can restore bounded revenue loss.

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Matteo Castiglioni, Junjie Chen, Yingkai Li. 2025-07-15. Algorithmic Fair Contracts. https://arxiv.org/abs/2507.11214

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