In ATM systems, the massive number of interactin entities makes it difficult to predict the system-wide effects that innovations might have. Here, we present the approach proposed by the project Domino to assess such effects and identify the impact that innovations might bring for the different stake-holders, based on agent-based modelling and complex network science. Domino will model scenarios mirroring different system innovations which change the agents’ actions and behaviour. Suitable network metrics are needed to evaluate the effect of innovations on the network functioning. We review existing centrality and causality metrics and show their limitations in characterising the network by applying them to a dataset of US flights. We finally suggest improvements that should be introduced to obtain new metrics answering to Domino’s needs
