论文标题
在严重不确定性条件下的多目标决策
Multi-Target Decision Making under Conditions of Severe Uncertainty
论文作者
论文摘要
在（严重）不确定性下的决策问题中的后果质量通常必须同时在不同目标（目标）之间进行比较。此外，对后果在各种目标下的表现的评估通常在其测量规模上有所不同，从而经典地是纯正的或完全基本的。在本文中，我们将最新的发展从抽象的决策理论转移到了不完整的优先和概率信息到此多目标环境中，并通过利用（可能的）部分基本和部分概率信息来显示如何比帕累托命令给出更有用的比较决策订单。我们讨论了决策选项之间提出的订单的一些有趣属性，并展示了如何通过线性优化对它们进行具体计算。我们通过在比较不同绩效指标下的算法的背景下在人工（但相当真实的）示例中演示我们的框架来结束论文。
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how -- by exploiting the (potentially) partial cardinal and partial probabilistic information -- more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.
