Air traffic management (ATM) comprises a highly complex socio-technical system that keeps air traffic flowing safely and efficiently, worldwide, every minute of the year. Over the last few decades, several ambitious ATM performance improvement programmes have been undertaken. Such programmes have mostly delivered local technological solutions, whilst corresponding ATM performance improvements have fallen short of stakeholder expectations. In hindsight, this can be substantially explained from a complexity science perspective: ATM is simply too complex to address through classical approaches such as system engineering and human factors. In order to change this, complexity science has to be embraced as ATM's 'best friend'. The applicability of complexity science paradigms to the analysis and modelling of future operations is driven by the need to accommodate long-term air traffic growth within an already-saturated ATM infrastructure.

Glossary of Common Terms; Chapter 1 Introduction, Marc Bourgois; Chapter 2 Complex Network Theory, Andrew Cook Dr, Massimiliano Zanin Dr; Chapter 3 Complex Networks in Air Transport, Fabrizio Lillo, Rosario N. Mantegna, Salvatore Miccichè; Chapter 4 Uncertainty, Damián Rivas, Rafael Vazquez; Chapter 5 Resilience, Henk A.P. Blom Dr, Soufiane Bouarfa; Chapter 6 Emergent Behaviour, Henk A.P. Blom Dr, Mariken H.C. Everdij Dr, Soufiane Bouarfa; Chapter 7 Data Science, Massimiliano Zanin Dr, Andrew Cook Dr, Seddik Belkoura; Chapter 8 Conclusions and a Look Ahead, David Pérez;