Policymaking during a pandemic can be extremely challenging. As COVID-19 is a new disease and its global impacts are unprecedented, decisions are taken in a highly uncertain, complex, and rapidly changing environment. In such a context, in which human lives and the economy are at stake, we argue that using ideas and constructs from modern decision theory, even informally, will make policymaking a more responsible and transparent process.

We propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influences the optimal level of mitigation. We disentangle the role of preferences from the structure of model uncertainty, which we define by means of a simple measure of disagreement across models. With data from an expert elicitation about climate change catastrophes, we quantify the relative importance of these two effects and calibrate a numerical integrated assessment model of climate change. The results indicate that the structure of model uncertainty, and specifically how model disagreement varies in abatement, is the key driver of optimal abatement and that model uncertainty warrants a higher level of climate change mitigation. This paper was accepted by Manel Baucells, decision analysis.