[Talk] Bayesian Decision Analysis for climate decision-making: Sensitivity to decision attributes

Bayesian Decision Analysis (BDA) is a framework for decision-making given an uncertain state of nature, making it a powerful tool for climate change adaptation decision-making. These decisions rely on the estimation of risk, which is modelled using information about hazard (the source of potential damage), exposure (the amount of damage experienced), and vulnerability (the level of susceptibility to damage), each of which has a high degree of uncertainty. We apply BDA to an idealised example application of adaptation options to combat the effects of heat-stress. Previous work has investigated the sensitivity of BDA to variations in hazard, exposure, and vulnerability. We build on these analyses to investigate how variations in the attributes of individual adaptation options, such as costs, affect the decision outcome. We perform uncertainty and sensitivity analysis to identify where the optimal decision is robust to variations in cost and to identify the most influential inputs and investigate how these vary spatially. We find a low degree of robustness in the optimal decision across most regions and an array of spatial patterns in sensitivity to the various financial cost attributes of the decision-making framework. Understanding which factors have the greatest influence on the optimal decision is crucial for transparent and robust climate adaptation decision-making.