We study the problem of dynamically allocating indivisible items 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. We further impose the contiguity constraint on items 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's 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而产生消极结果,我们允许进行调整,以实现公平,从而达到尽可能减少调整次数的目标。关于限制性添加剂或一般相同的估值,我们表明,可以免费达到一个项目(EF1)的无嫉妒程度。对于添加剂估值,我们给出一种EF1算法,需要调整美元(mT),其中美元是所有代理商之间对物品的不同估值的最大数额。我们进一步对物品施加毗连限制,要求每个代理商获得一整块连续的项目。我们提出广泛的结果,用一个相加近因或EF1实现相称性。特别是,我们为相同的估值设定了相应的下限和上限,以达到大致的相称性。我们还表明,如果估值不相同,则没有任何重大改进的余地。我们的结果显示,在毗连和不相连不相联的环境下,都存在相同的和不相同的案例之间存在很大的差异。我们所有的积极结果都是计算有效的。