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。 这项研究计划的目的是阐明如何适当和有效地处理无法解决的模糊性，这些模糊性困扰着从广泛的工程、管理和治理背景中出现的大量重要决策问题。特别是，该计划将促进理论和算法发现，预计这些发现将对以下应用产生严重影响： ·魁北克水电公司在这样一种环境下进行水电生产的长期规划，即很难确定未来的水流，但又承受不起邻近领土的意外洪灾； ·考虑到传输时间的不确定性和预先达成的服务质量协议，视频或罗杰斯等电信公司通过互联网进行数据包路由； ·在气候系统的敏感变量无法测量时，制定有效的环境政策，以防止全球变暖； ·沃尔玛或西尔斯等大型零售商的供应链管理，当一些供应商位于海外，还不能被认为是可靠的时候； ·在客户面临过多选择、市场操纵者猖獗的市场中，为亚马逊等在线商务设计推荐系统； ·在未来资产价值不能依赖分销模式的背景下，加拿大银行对金融衍生品的投资组合管理和定价。

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. The objective of this research program is to shed some light on how to properly and efficiently handle the unresolvable ambiguity that plagues a great number of important decision problems emerging from a wide range of engineering, management and governance contexts. In particular, this program will instigate theoretical and algorithmic discoveries that are expected to have serious impact for applications such as: • Hydro-Québec’s long-term planning of hydroelectric production in an environment where it is difficult to determine future water flows and yet where one cannot afford accidental flooding of neighbouring territories; • Data routing over the Internet of packets by telecommunication companies like Videotron or Rogers, given the uncertainty of transmission times and pre-established agreements about quality of service; • Development of efficient environmental policies to protect against global warming when sensitive variables of climate systems cannot be measured; • Supply chain management for big retailers like Walmart or Sears, when some suppliers are located offshores and cannot yet be considered reliable; • Design of recommendation systems for online commerce like Amazon in a market where clients are faced with a plethora of choices and where market manipulators thrive; • Portfolio management and pricing of financial derivatives by Canadian banks in a context where one cannot rely on a distribution model for future asset values.