Fair Contracts
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909698340421632 |
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| author | Castiglioni, Matteo Chen, Junjie Li, Yingkai |
| author_facet | Castiglioni, Matteo Chen, Junjie Li, Yingkai |
| contents | We introduce and study the problem of designing optimal contracts under fairness constraints on the task assignments and compensations. We adopt the notion of envy-free (EF) and its relaxations, $ε$-EF and envy-free up to one item (EF1), in contract design settings. Unlike fair allocations, EF contracts are guaranteed to exist. However, computing any constant-factor approximation to the optimal EF contract is NP-hard in general, even using $ε$-EF contracts. For this reason, we consider settings in which the number of agents or tasks is constant. Notably, while even with three agents, finding an EF contract better than $2/5$ approximation of the optimal is NP-hard, we are able to design an FPTAS when the number of agents is constant, under relaxed notions of $ε$-EF and EF1. Moreover, we present a polynomial-time algorithm for computing the optimal EF contract when the number of tasks is constant. Finally, we analyze the price of fairness in contract design. We show that the price of fairness for exact EF contracts can be unbounded, even with a single task and two agents. In contrast, for EF1 contracts, the price of fairness is bounded between $Ω(\sqrt{n})$ and $O(n^2)$, where $n$ is the number of agents. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_11214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fair Contracts Castiglioni, Matteo Chen, Junjie Li, Yingkai Computer Science and Game Theory We introduce and study the problem of designing optimal contracts under fairness constraints on the task assignments and compensations. We adopt the notion of envy-free (EF) and its relaxations, $ε$-EF and envy-free up to one item (EF1), in contract design settings. Unlike fair allocations, EF contracts are guaranteed to exist. However, computing any constant-factor approximation to the optimal EF contract is NP-hard in general, even using $ε$-EF contracts. For this reason, we consider settings in which the number of agents or tasks is constant. Notably, while even with three agents, finding an EF contract better than $2/5$ approximation of the optimal is NP-hard, we are able to design an FPTAS when the number of agents is constant, under relaxed notions of $ε$-EF and EF1. Moreover, we present a polynomial-time algorithm for computing the optimal EF contract when the number of tasks is constant. Finally, we analyze the price of fairness in contract design. We show that the price of fairness for exact EF contracts can be unbounded, even with a single task and two agents. In contrast, for EF1 contracts, the price of fairness is bounded between $Ω(\sqrt{n})$ and $O(n^2)$, where $n$ is the number of agents. |
| title | Fair Contracts |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2507.11214 |