Γ-maximin, Γ-maximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of linear programs. In this study, we present new algorithms for these criteria and compare their performance to existing standard algorithms. Specifically, we use efficient ways, based on previous work, to find common initial feasible points for these algorithms. Exploiting these initial feasible points, we develop early stopping criteria to determine whether gambles are either Γ-maximin, Γ-maximax or interval dominant. We observe that the primal-dual interior point method benefits considerably from these improvements. In our simulation, we find that our proposed algorithms outperform the standard algorithms when the size of the domain of lower previsions is less or equal to the sizes of decisions and outcomes. However, our proposed algorithms do not outperform the standard algorithms in the case that the size of the domain of lower previsions is much larger than the sizes of decisions and outcomes.

当仅能部分识别概率分布时，Γ-maximin，Γ-maximax和区间优势是在严重不确定性下进行决策的熟悉的决策标准。可以通过求解线性程序的序列来应用这三个条件。在这项研究中，我们提出了针对这些标准的新算法，并将其性能与现有标准算法进行了比较。具体来说，我们根据以前的工作使用有效的方法来找到这些算法的常见初始可行点。利用这些初始可行点，我们开发了早期停止标准，以确定赌博是Γ-maximin，Γ-maximax还是区间主导。我们观察到，原始对偶内点法从这些改进中受益匪浅。在我们的模拟中 我们发现，当较低预想域的大小小于或等于决策和结果的大小时，我们提出的算法优于标准算法。但是，在较低预视域的大小比决策和结果的大小大得多的情况下，我们提出的算法不会优于标准算法。