Optimizing decisions in situations with unresolved ambiguity

通常需要在对正在解决的问题的某些参数不完全了解的情况下做出决定。例如，在库存管理问题中，产品的未来市场需求可能会发生变化，或者在资产未来价值不确定的投资组合选择问题中就是如此。虽然在许多情况下，可以使用分布模型充分描述不确定性的性质，但现在有越来越多的证据表明，在大多数情况下，这种模型不可能准确和明确地识别。事实上，在“大数据”时代，决策者从海量的历史数据中提取分布信息是一种常见的做法，但这样的过程必然会让人对分布以及这种分布实际上在多大程度上类似于未来结果的分布缺乏准确的描述。人们还可能会想起最近的金融危机之后出现的关于如何对共同实现事件的可能性进行建模的辩论和争议(例如，在债务抵押债券的定价方面)，甚至关于如何量化风险(例如，使用在险价值或《巴塞尔协议三》所提议的银行监管中的预期缺口)的辩论和争议。这两场辩论都对需要降低风险的工业工程问题产生了严重影响。

Decisions often need to be made in situations where one has incomplete knowledge about some of the parameters of the problem being addressed. This is for example the case in an inventory management problem where the future market demand for a product might be subject to change, or in a problem of portfolio selection where the future value of assets is uncertain. While there are many situations in which the nature of uncertainty can be fully characterized using distribution models, there is now a growing amount of evidence that indicates that such models are in most situation impossible to identify precisely and unequivocally. Indeed, in the era of “Big data”, it is now common practice for decision maker to extract distribution information from immense collection of historical data, yet such procedures necessarily leave one with an imprecise characterization of the distribution and of how much this distribution actually resembles the distribution of future outcomes. One might also evoke the debates and controversy that have emerged following the recent financial crisis regarding how to model the likelihood of joint realization of events (e.g. in the pricing of collateralized debt obligations) and even regarding how to quantify risk (e.g. the use of value at risk or expected shortfall in banking regulations as proposed by the Basel III accords). Both debates have serious implications for industrial engineering problems where risk needs to be mitigated.