Optimizing decisions in situations with unresolved ambiguity

决策往往需要在人们对所处理问题的某些参数不完全了解的情况下做出。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，或者在资产的未来价值不确定的投资组合选择问题中，情况就是如此。虽然在许多情况下，不确定性的性质可以用分布模型来充分表征，但现在越来越多的证据表明，在大多数情况下，这种模型是不可能精确和明确地识别的。事实上，在“大数据”时代，决策者从大量历史数据中提取分布信息现在是常见的做法，但这样的程序必然会给人们留下一个不精确的分布特征，以及这个分布实际上与未来结果的分布有多少相似。人们还可能会想起最近的金融危机之后出现的辩论和争议，即如何对共同实现事件的可能性进行建模（例如在债务抵押债券的定价中），甚至如何量化风险（例如按照巴塞尔三协定的建议，使用风险价值或银行监管中的预期缺口）。这两种争论都对需要减轻风险的工业工程问题产生了严重的影响。

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.