Optimizing decisions in situations with unresolved ambiguity

决策通常需要在人们对正在处理的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，就是这种情况。虽然在许多情况下，不确定性的本质可以用分布模型来充分表征，但现在有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出来的。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是一种常见的做法，但这样的程序必然会让人对分布的特征以及这种分布与未来结果的分布有多相似留下不精确的印象。人们还可能会想起最近金融危机之后出现的关于如何对事件共同实现的可能性进行建模（例如，在抵押债务债券的定价中），甚至关于如何量化风险（例如，使用风险价值或巴塞尔协议III提议的银行监管的预期不足）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的影响。****本研究计划的目的是阐明如何正确有效地处理困扰大量重要决策问题的无法解决的模糊性，这些问题来自广泛的工程，管理和治理环境。特别是，该计划将激发理论和算法发现，预计将对以下应用产生严重影响：*** hydro - qubec在难以确定未来水流的环境中长期规划水电生产，但又不能承受邻近地区的意外洪水；***考虑到传输时间的不确定性和预先建立的服务质量协议，由Videotron或Rogers等电信公司在互联网上进行数据包路由；***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，因为一些供应商位于海外，还不可靠；***为像亚马逊这样的在线商务设计推荐系统，在这个市场上，客户面临着过多的选择，市场操纵者猖獗；***加拿大银行在不能依赖未来资产价值分布模型的情况下的金融衍生品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.***