A framework based on statistical analysis and stakeholders' preferences to inform weighting in composite indicators. (November 2021) - Lindén, David; Cinelli, Marco; Spada, Matteo; Becker, William; Gasser, Patrick; Burgherr, Peter
Abstract: Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information to the CI. A measure of information transfer from each indicator is proposed along with two weight-optimization methods, which allow the weights to be adjusted to achieve either a targeted or maximized information transfer. The tools presented in this paper are applied to a case study for resilience assessment of energy systems, demonstrating how they can support the tailored development of CIs. These findings enable analysts bridging the statistical properties of the index with the weighting preferences from the stakeholders. They can thus choose a weighting scheme and possibly modify the index while achieving a more consistent (by correlation) index. Highlights: Composite indicator development is enhanced with advanced statistical analysis. Tools to assess the information transferred from indicators to the index are proposed. Two weight-optimization methods allow adjusting information transfer to the index. The approaches are applied to a real case study on resilience of electricity supply.