In a web-based review platform, papers from various research fields must be assigned to a group of reviewers. Each paper has an inherent cost, which represents the effort required for reading and evaluating it (e.g., the paper's length). Reviewers can bid on papers they are interested in, and if they are assigned a paper they have bid on, no cost is incurred. Otherwise, the inherent cost $c(e)$ for paper $e$ applies. We capture this with a model of restricted additive costs: every item $e$ has a cost $c(e)$, and each agent either incurs $0$ or $c(e)$ for $e$. In this work, we study how to allocate such chores fairly and efficiently. We propose an algorithm for computing allocations that are both EFX and MMS. Furthermore, we show that our algorithm achieves a $2$-approximation of the optimal social cost, and the approximation ratio is optimal. We also show that slightly weaker fairness guarantees can be obtained if one requires the algorithm to run in polynomial time.

翻译：在基于网络的评审平台中，来自不同研究领域的论文需分配给一组评审人。每篇论文具有固有成本，代表阅读与评估该论文所需的工作量（例如论文长度）。评审人可对感兴趣的论文进行投标，若被分配到的论文已由其投标，则无需承担成本；否则，将承担论文$e$的固有成本$c(e)$。我们通过限制性附加成本模型对此进行刻画：每个物品$e$具有成本$c(e)$，每位代理人要么为$e$产生$0$成本，要么产生$c(e)$。本文研究如何公平高效地分配此类苦差。我们提出一种算法，可计算同时满足EFX（无妒忌-自由至多一项）和MMS（最大最小份额）的分配方案。进一步证明该算法可实现最优社会成本的$2$倍近似，且该近似比是最优的。同时表明，若要求算法在多项式时间内运行，可得到略弱化的公平性保障。