Managing Severe Uncertainty

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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.