Using tensor calculus for scenario modelling

We have demonstrated the benefits of sparse tensor calculus for finite-difference techniques that are widely applied to Integrated Assessment (IA). Using a tensor toolbox for Matlab, we have developed efficient code for progressing a system of state variables connected by a large variety of interaction types. Using a small example of twenty variables across three countries, we demonstrate how the tensor formalism allows not only for compact and fast scenario modelling, but also for straightforward implementation of sensitivity and Monte-Carlo analyses, as well as Structural Decomposition Analysis. In particular, we show how sparse tensor code can be exploited in order to search for potentially important, but yet unknown relationships in the interaction network between all variables.