David Tuckett (University College London), Lenny Smith (London School of Economics), Gerd Gigerenzer (MPI Human Development, Berlin), Jürgen Jost (MPI Mathematics in the Sciences, Leipzig, Santa Fe Institute)
The current Corona pandemic presents decision makers with situations where the range of possible actions and the probabilities of possible outcomes are not known or even not imagined.
Because data are unavailable or contestable, using them to make sense of exactly what is going on or how the pandemic will play out is unreliable. Actions are untested, their acceptance by populations is not guaranteed and long-term societal and economic impacts are unclear. Should we aim to eradicate, to slow, or accept deaths of the vulnerable and minimize the collateral economic damage?
Given our data, we may want to extrapolate a curve to make a prediction. It would be fantastic to have a perfect fit. But from limited statistical data, we cannot get that. Even worse, an optimal statistical fit (red line) is far inferior to a cone of possibilities conditioned on clearly stated assumptions. That is an important insight.
Radical Uncertainty (RU) is when quantifying costs and consequences is contestable but we must choose. Current scientific decision theory, mostly based on a rationality paradigm that aims at optimal decisions in contexts of known outcomes and their probabilities, is silent on what to do. The financial crisis, the climate challenge and now COVID-19 emphasize the need to fill the gap.
Our collaboration focuses on how to aid decision-makers to select data inquiringly from diverse sources, to recognize structural instabilities and to imagine possible big surprises. It emphasizes systematic ways to reduce complexity and to identify essential variables which have the largest effect and which we can control. Can we understand the relations between them, for instance, by checking the effects of small perturbations and their propagation? Can we reduce general vulnerability? Which are good enough heuristics to cut through details, and what kinds of narratives make sense of complicated developments?
Crucially, optimization in RU is dangerous. Its premises are not satisfied. It blinds us to the need to embrace diversity and experiment. It installs a blame rather than a creative culture and causes fragility rather than resilience - whether designing medical facilities or supply chains, for instance.
Decisively, RU necessarily evokes ambivalence - reasons for and against choices and the accompanying emotions of doubt or excitement. To make good decisions under uncertainty decision-makers must act creatively to avoid paralysis, while recognizing the possibility of failure and accompanying anxiety. Decisions can work if supported by and communicated with a conviction narrative (CN). A CN generates confidence and creates cooperative publics. Good CN’s are the product of enquiring into and facing doubt transparently in conversation. Bad CN’s are the outcome of dictatorial assertion, attraction to phantastic object solutions and groupthink, creating “certainty”.
Research progress requires novel cross-disciplinary research combining mathematics, machine-learning and physics with economics, engineering, social science and psychology.