Decision Making under Deep Uncertainty
Decision Making under Deep Uncertainty
复制标题
深度不确定性下的决策
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Popper
中科院分区：
文献类型：
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作者：
V. Marchau;W. Walker;P. Bloemen;S. Popper
未来的决策取决于对变化的预期。这种预期变得越来越困难，因此当我们寻求使短期决策符合长期目标或为罕见事件做好准备时，就会产生焦虑。决策者和他们所依赖的分析师有充分的理由对自己正确预测未来技术、经济和社会发展、他们试图改进的系统的未来变化或利益相关者对系统结果的多样性和随时间变化的偏好的能力感到信心下降。例如，考虑与气候变化后果相关的决策、未来对流动性的需求和提供手段、大型基础设施项目的规划、未来依赖的能源的选择、基因组学在医疗保健中的作用，或者城市将如何发展。或者想想自然灾害、金融危机或恐怖袭击等罕见事件。 1.1 在决策中考虑不确定性的必要性 未来的决策取决于对变化的预期。这种预期变得越来越困难，因此当我们寻求使短期决策符合长期目标或为罕见事件做好准备时，就会产生焦虑。十V。 A. W. J. Marchau (B) 奈梅亨管理学院，拉德堡德大学，荷兰奈梅亨 电子邮件：v.marchau@fm.ru.nl W. E. Walker 代尔夫特理工大学技术、政策与管理学院，荷兰代尔夫特 P. J. T. M. Bloemen 职员 三角洲计划专员，荷兰海牙基础设施和水管理部 电子邮件： pieter.bloemen@deltacommissaris.nl S. W. Popper Pardee 兰德研究生院，兰德公司，美国加利福尼亚州圣莫尼卡 © 作者 2019 V. A. W. J. Marchau 等人。 （编），深度不确定性下的决策，https://doi.org/10.1007/978-3-030-05252-2_1 1 2 V. A. W. J. Marchau 等人。愿景制定者以及他们所依赖的分析师有充分的理由对自己正确预测未来技术、经济和社会发展、他们试图改进的系统的未来变化或利益相关者对系统结果的多样性和随时间变化的偏好的能力感到信心下降。例如，考虑与气候变化后果相关的决策、未来对流动性的需求和提供方式、大型基础设施项目的规划、未来依赖的能源的选择、基因组学在医疗保健中的作用，或者城市将如何发展。或者想想自然灾害、金融危机或恐怖袭击等罕见事件。这些主题的特点都是所谓的“深度不确定性”。在这些情况下，专家不知道或决策各方无法就以下方面达成一致：(i) 系统的外部背景，(ii) 系统如何工作及其边界，和/或 (iii) 系统的利益结果和/或其相对重要性（Lempert 等，2003）。随着时间的推移，为应对不可预测的不断变化的情况而采取的行动也会产生深度不确定性（Haasnoot 等，2013）。从广义上讲，不确定性（无论是否深刻）可以简单地定义为关于未来、过去或当前事件的有限知识（Walker et al. 2013）。就决策而言，不确定性是指现有知识与决策者做出最佳政策选择所需的知识之间的差距。这种不确定性显然涉及主观性，因为它与对现有知识的满意度有关，而现有知识的满意度受到决策者（以及决策过程中涉及的各个参与者）的基本价值观和观点的影响。但当隐含的假设未经检验或质疑时，这本身就成为一个陷阱。不确定性可能与感兴趣问题的所有方面相关（例如，包含决策域的系统、系统外部的世界、系统的结果以及利益相关者对系统各种结果的重视程度）。英吉利海峡隧道的规划说明了忽视不确定性的危险。图 1.1 显示了不同研究的预测和英吉利海峡铁路隧道的实际乘客数量。大多数研究都没有考虑低成本航空公司的竞争和渡轮运营商的价格反应等因素。这导致了对隧道收入和市场地位的严重高估（Anguara 2006），给该项目带来了毁灭性的后果。 1994 年开通 20 年后，它的载客量仍然没有达到预期。1 由于存在深刻的不确定性，气候变化对将分析见解引入政策决策提出了根本性挑战。气候变化通常被认为是深度不确定性的一个来源。为未来气候变化情景分配概率的问题尤其有争议。尽管许多人认为排放量的科学不确定性根本不允许我们得出未来气候状态的可靠概率分布，但其他人反驳说，1预计的交通增长仍远未实现，“2017年，大约有2100万名乘客通过所有服务穿越英吉利海峡隧道”http://www.eurotunnelgroup.com/uk/eurotunnel-group/operations/traffic-figures/。
Decisionmaking for the future depends on anticipating change. And this anticipation is becoming increasingly difficult, thus creating anxiety when we seek to conform short-term decisions to long-term objectives or to prepare for rare events. Decisionmakers, and the analysts upon whom they rely, have had good reason to feel decreasing confidence in their ability to anticipate correctly future technological, economic, and social developments, future changes in the system they are trying to improve, or the multiplicity and time-varying preferences of stakeholders regarding the system’s outcomes. Consider, for example, decisionmaking related to the consequences of climate change, the future demand for and means for providing mobility, the planning of mega-scale infrastructure projects, the selection of energy sources to rely on in the future, the role of genomics in health care, or how cities will develop. Or think of rare events like a natural disaster, a financial crisis, or a terrorist attack. 1.1 The Need for Considering Uncertainty in Decisionmaking Decisionmaking for the future depends on anticipating change. And this anticipation is becoming increasingly difficult, thus creating anxiety when we seek to conform short-term decisions to long-term objectives or to prepare for rare events. DeciV. A. W. J. Marchau (B) Nijmegen School of Management, Radboud University, Nijmegen, The Netherlands e-mail: v.marchau@fm.ru.nl W. E. Walker Faculty of Technology, Policy & Management, Delft University of Technology, Delft, The Netherlands P. J. T. M. Bloemen Staff Delta Programme Commissioner, Ministry of Infrastructure and Water Management, The Hague, The Netherlands e-mail: pieter.bloemen@deltacommissaris.nl S. W. Popper Pardee RAND Graduate School, RAND Corporation, Santa Monica, CA, USA © The Author(s) 2019 V. A. W. J. Marchau et al. (eds.), Decision Making under Deep Uncertainty, https://doi.org/10.1007/978-3-030-05252-2_1 1 2 V. A. W. J. Marchau et al. sionmakers, and the analysts upon whom they rely, have had good reason to feel decreasing confidence in their ability to anticipate correctly future technological, economic, and social developments, future changes in the system they are trying to improve, or the multiplicity and time-varying preferences of stakeholders regarding the system’s outcomes. Consider, for example, decisionmaking related to the consequences of climate change, the future demand for and means for providing mobility, the planning of mega-scale infrastructure projects, the selection of energy sources to rely on in the future, the role of genomics in healthcare, or how cities will develop. Or think of rare events like a natural disaster, a financial crisis, or a terrorist attack. These topics are all characterized by what can be called “deep uncertainty.” In these situations, the experts do not know or the parties to a decision cannot agree upon (i) the external context of the system, (ii) how the system works and its boundaries, and/or (iii) the outcomes of interest from the system and/or their relative importance (Lempert et al. 2003). Deep uncertainty also arises from actions taken over time in response to unpredictable evolving situations (Haasnoot et al. 2013). In a broad sense, uncertainty (whether deep or not) may be defined simply as limited knowledge about future, past, or current events (Walker et al. 2013). With respect to decisionmaking, uncertainty refers to the gap between available knowledge and the knowledge decisionmakers would need in order to make the best policy choice. This uncertainty clearly involves subjectivity, since it relates to satisfaction with existing knowledge, which is colored by the underlying values and perspectives of the decisionmaker (and the various actors involved in the decisionmaking process). But this in itself becomes a trap when implicit assumptions are left unexamined or unquestioned. Uncertainty can be associated with all aspects of a problem of interest (e.g., the system comprising the decision domain, the world outside the system, the outcomes from the system, and the importance stakeholders place on the various outcomes from the system). The planning for the Channel Tunnel provides an illustration of the danger of ignoring uncertainty. Figure 1.1 shows the forecasts from different studies and the actual number of passengers for the rail tunnel under the English Channel. The competition from low-cost air carriers and the price reactions by operators of ferries, among other factors, were not taken into account in most studies. This resulted in a significant overestimation of the tunnel’s revenues and market position (Anguara 2006) with devastating consequences for the project. Twenty years after its opening in 1994, it still did not carry the number of passengers that had been predicted.1 Climate change presents a fundamental challenge to bringing analytical insight into policy decisions because of deep uncertainties. Climate change is commonly mentioned as a source of deep uncertainty. The question of assigning probabilities to future scenarios of climate change is particularly controversial. While many argue that scientific uncertainty about emissions simply does not allow us to derive reliable probability distributions for future climate states, others counter by saying that the 1The projected traffic growth is still far from being achieved, “In 2017, about 21 million passengers, on all services, have travelled through the Channel Tunnel” http://www.eurotunnelgroup.com/uk/ eurotunnel-group/operations/traffic-figures/.
影响因子：
6.4
作者：
Matrosov, Evgenii S.;Woods, Ashley M.;Harou, Julien J.
通讯作者：
Harou, Julien J.
快与慢的思考：跨时间尺度的优化分解
DOI：
10.1109/cdc.2017.8263834
发表时间：
2017
期刊：
56th IEEE Conference on Decision and Control
影响因子：
--
作者：
Goel, Gautam;Chen, Niangjun;Wierman, Adam
通讯作者：
Wierman, Adam