Most of the existing algorithms for fair division do not consider externalities. Under externalities, the utility an agent obtains depends not only on its allocation but also on the allocation of other agents. An agent has a positive (negative) value for the assigned goods (chores). This work focuses on a special case of externality, i.e., an agent receives positive or negative value for unassigned items independent of which other agent gets it. We show that it is possible to adapt existing algorithms using a transformation to ensure certain fairness and efficiency notions in this setting. Despite the positive results, fairness notions like proportionality need to be re-defined. Further, we prove that maximin share (MMS) may not have any multiplicative approximation in this setting. Studying this domain is a stepping stone towards full externalities where ensuring fairness is much more challenging.
翻译:公平分配的现有算法大多不考虑外部因素。 在外部因素下,代理商获得的效用不仅取决于其分配情况,而且取决于其他代理商的分配情况。代理商对转让的货物(工作)具有积极的(消极的)价值。这项工作侧重于外在性的特殊情况,即代理商对未分配的物品获得正或负的价值,而其他代理商则获得这种价值。我们表明,利用现有的算法进行转换以确保这一环境的某种公平和效率概念是可能的。尽管取得了积极的结果,但像相称性这样的公平概念需要重新界定。此外,我们证明,在这种环境下,最大份额(MMS)可能没有任何重复的近似之处。研究这一领域是走向完全的外部因素的踏脚石,确保公平性更具挑战性。