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Managing Severe Uncertainty

管理严重的不确定性

基本信息

批准号：
AH/J006033/1
负责人：
Richard Bradley
金额：
$ 77.89万
依托单位：
London School of Economics and Political Science
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=AH%2FJ006033%2F1
关键词：
Managing Severe Uncertainty

项目摘要

Many of the important decisions that need to be made by individuals, groups or institutions are made under conditions of considerable uncertainty about the factors upon which these decisions depend. There are two essential dimensions to this uncertainty: (i) scientific or modelling uncertainty (we don't know what the state of the world is and under which dynamical law it will evolve) and (ii) option uncertainty (we do not know what our options are and what would happen if we were to exercise one or another of them. Crucially, these dimensions interact: modelling uncertainty contributes to option uncertainty, for instance. Climate change policy-making exemplifies both forms of uncertainty. On the side of scientific uncertainty, we face the problem that climate models are imperfect and non-linear, which undermines standard forecasting procedures. In particular, climate models are known to be strongly imperfect in the sense that they do not even approximately mirror all the real-world target systems. Recent results suggest that this leads to the breakdown of probabilistic forecasts, thus depriving us of the most powerful and widely used tool for making predictions. On the side of option uncertainty, we know little about the potential effects (and side-effects) of some of the proposed measures for mitigation and adaption - carbon capture and storage, creating clouds from seawater, placing reflectors in the outer atmosphere, and so on - or what other mechanisms or technologies might be developed. But despite a recent resurgence of interest in rational decision making in situations in which agents lack full probabilistic information (commonly termed situations of ambiguity), theoretical approaches remain rather undeveloped. Decision theorists' discussions of the first kind of uncertainty are even more embryonic, as does discussion of how these forms of uncertainty are related to one anotherTaking climate science and policy as our main case study, we will look at two different families of questions. 1. What are the reasons for scientific uncertainty regarding climate change? This involves questions such as: Why and how do probabilistic forecasts break down? What model imperfections 'destroy' predictive power? How might we detect these imperfections? How can models be improved? 2. How should one make policy decisions under uncertainty? This involves questions such as: How can we make decisions when we lack determinate probabilistic predictions for crucial variables, such as local precipitation at future dates, upon which our decisions depend (this concerns scientific uncertainty)? How should we think about our possible policy options when we don't know which of them will be feasible (this concerns option uncertainty)? This three-year project, bringing together academics at the LSE with expertise in scientific modelling, decision-making and climate policy, will develop answers to these questions in a way that reflects their interdependence. The kind of information and level of accuracy required in order to make policy decisions should, for instance, inform strategies for dealing with scientific uncertainty. Correspondingly, policy decisions should be made in a way that recognises scientific uncertainty and maximises their robustness in the face of different possible resolutions of the uncertainty.

许多需要个人、团体或机构作出的重要决定是在这些决定所依赖的因素相当不确定的情况下作出的。这种不确定性有两个基本方面：（i）科学或建模的不确定性（我们不知道世界的状态是什么，以及它将在哪种动力学定律下演变）和（ii）选项的不确定性（我们不知道我们的选项是什么，以及如果我们行使其中一个或另一个选项会发生什么。至关重要的是，这些维度相互作用：例如，建模的不确定性会导致期权的不确定性。气候变化政策的制定使这两种形式的不确定性更加突出。在科学不确定性方面，我们面临的问题是气候模型不完美和非线性，这破坏了标准的预测程序。特别是，气候模型被认为是非常不完美的，因为它们甚至不能近似地反映所有真实世界的目标系统。最近的研究结果表明，这导致了概率预测的崩溃，从而使我们失去了最强大和最广泛使用的预测工具。在选择的不确定性方面，我们对一些拟议的减缓和适应措施的潜在影响（和副作用）知之甚少-碳捕获和储存，从海水中制造云，在外层大气中放置反射器，等等-或者可能开发的其他机制或技术。但是，尽管最近的兴趣复苏理性决策的情况下，代理人缺乏充分的概率信息（通常称为情况下的模糊性），理论方法仍然相当不发达。决策理论家对第一种不确定性的讨论甚至更加萌芽，关于这些形式的不确定性如何相互关联的讨论也是如此。以气候科学和政策为主要案例研究，我们将研究两类不同的问题。1.关于气候变化的科学不确定性的原因是什么？这涉及到诸如以下问题：概率预测为什么会失败？什么样的模型缺陷会“破坏”预测能力？我们如何发现这些缺陷呢？如何改进模型？2.如何在不确定性条件下进行决策？这涉及到这样的问题：当我们缺乏对关键变量的确定性概率预测时，我们如何做出决策，比如我们的决策所依赖的未来日期的局部降水量（这涉及到科学的不确定性）？当我们不知道哪一种政策是可行的（这涉及到选择的不确定性）时，我们应该如何思考我们可能的政策选择？这个为期三年的项目汇集了伦敦经济学院在科学建模，决策和气候政策方面的专业知识，将以反映其相互依存性的方式为这些问题提供答案。例如，决策所需的信息种类和准确程度应成为应对科学不确定性的战略依据。相应地，政策决定应该以一种承认科学不确定性的方式做出，并在面对不确定性的不同可能解决方案时最大限度地提高其稳健性。