We study the problem of dynamically allocating indivisible items with nonnegative valuations to a group of agents in a fair manner. Due to the negative results to achieve fairness when allocations are irrevocable, we allow adjustments to make fairness attainable with the objective to minimize the number of adjustments. For restricted additive or general identical valuations, we show that envy-freeness up to one item (EF1) can be achieved at no cost. For additive valuations, we give an EF1 algorithm that requires $O(mT)$ adjustments, where $m$ is the maximum number of different valuations for items among all agents and $T$ is the number of items. We further impose the contiguity constraint on items such that items are arranged on a line by the order they arrive and require that each agent obtains a consecutive block of items. We present extensive results to achieve either proportionality with an additive approximate factor or EF1. In particular, we establish matching lower and upper bounds for identical valuations to achieve approximate proportionality. We also show that it is hopeless to make any significant improvement when valuations are nonidentical. Our results exhibit a large discrepancy between the identical and nonidentical cases in both contiguous and noncontiguous settings. All our positive results are computationally efficient.
翻译:我们以公平的方式研究将不可分割的物品与非负性价值进行动态分配给一组代理商的问题。由于在不可撤销分配时实现公平性的负面结果,我们允许调整,以实现公平,以尽量减少调整的数量;关于限制性添加或一般相同的估值,我们表明,可以免费达到一个项目(EF1)的无嫉妒程度;关于添加值,我们给出一种EF1算法,要求以非负性价值调整美元,其中百万美元是所有代理商不同物品的不同估值的最大数目,美元是项目数量。我们进一步对物品实行连续性限制,规定物品按它们抵达的顺序排列,要求每个代理商获得连续的物品块。我们提出广泛的结果,即与一个添加系数或EF1的相称性,特别是,我们为相同的估值设定了较低和上限值,以达到大致的相称性。我们还表明,如果估值不相同,则不可能作出重大改进。我们的结果显示,在连续和非相同和不相同的情况下,完全一致的计算结果是相同的。