Managing Severe Uncertainty

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 another

Taking 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.

As numerous agencies and institutions at least sometimes have to make decisions under conditions of severe uncertainty, the potential long-term impact of an improved understanding of how this uncertainty can be managed is both very large and widespread. It is to be expected, however, that the principal beneficiaries in the short to medium term would be institutions with the resources to build systems for modelling relevant phenomena and relating these to decision making. Policy-making bodies (local and national government, public institutions such as the health services and central banks) and large private-sector institutions such as insurance companies and investment banks are salient examples.

Although we believe that a study of decision making under severe uncertainty has potential impact in a large number of policy areas, it is our intention to focus our attention in the short term on contributing to the formation of climate policy. There are three reasons for this. Firstly, as climate science will serve as the main case study, the project members will develop the kind of detailed understanding of policy issues necessary to contribute to debates on climate policy. Secondly, there are a number of national and international bodies (for example, the World Resources Institute, UNDP, IPCC, and the UKCP) that have already expressed awareness of problems posed by uncertainty and which are likely to be interested in the project's results. And thirdly, the project is ideally placed to have a maximum impact on both researchers and policy makers. LSE is home to the Grantham Research Institute on Climate Change and the Environment (incorporating the ESRC Centre for Climate Change Economics and Policy) and the Centre for the Analysis of Time Series which taken together, form one the world's largest research hub for the study of climate change and its effects on society. These research centres make LSE a unique place to do work on climate change. They provide the project with the right environment to strive, and with access to many key figures in the field. Furthermore, the Grantham Research Institute has a team of four full-time members of staff dedicated to communications and making an impact on policy and public debate, including a Policy and Communications Director, a Policy Communications Officer covering the policy community and large private companies, a Public Communications Officer covering the general public, and a Web Officer. Through this unusually large team, the institute is able to maintain a large network of close contacts in policy and in public debate. In the context of the current project, these contacts include decision-makers who deal on a day-to-day basis with the issue of severe uncertainty in climate policy. Examples include climate scientists (e.g. at the UK Met. Office), insurers and reinsurers (e.g. Lloyds of London and Munich Re), and members of government departments and agencies tasked with climate-change mitigation and adaptation (e.g. DECC, DEFRA and the Environment Agency).

Through these channels the project aims to influence the way in which climate predictions are made and about how they are used to inform climate policy. In the longer term lessons learnt could be carried over to other domains in which institutions must make decisions under severe uncertainty. Examples include infectious disease control, planning for natural disasters, health care decisions being made in an environment in which advances in medical technologies as well as health threats are largely unpredictable, and both investments in and regulation of financial markets.