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：在基于网络的审稿平台中，来自不同研究领域的论文需要分配给一组审稿人。每篇论文具有固有成本，代表阅读和评估该论文所需的工作量（例如论文长度）。审稿人可以对自己感兴趣的论文进行投标，若被分配到已投标的论文则无需承担成本；否则需承担论文$e$的固有成本$c(e)$。我们通过受限附加成本模型刻画此场景：每个项目$e$具有成本$c(e)$，每个智能体对$e$承担的成本为$0$或$c(e)$。本研究探讨如何公平且高效地分配此类杂务。我们提出一种同时满足EFX与MMS的分配计算算法。进一步证明该算法能实现最优社会成本的$2$倍近似，且该近似比是最优的。我们还证明，若要求算法在多项式时间内运行，则可获得稍弱的公平性保证。