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Motivation

Air Traffic Management (ATM) is a safety-critical complex socio-technical system which involves various types of emergent behaviour at multiple time scales.

For current ATM, various types of uncertainties and positive emergent behaviour have been well embedded through decades of evolutionary development. In view of the kind of changes that are being foreseen in the SESAR concept of operations beyond 2020 architecture and design, significant changes in emergent behaviours are unavoidable. As long as these novel emergent behaviours are not understood it is very well possible that these have negative effects on ATM performance under various uncertainties.

However, if emergent behaviour is identified and understood at architectural socio-technical system design level, then this understanding can be used to adapt the future ATM design such that emergent behaviours work in favour of future ATM.

The EMERGIA project will identify potentially negative emergent behaviours of SESAR2020+, and subsequently will improve the SESAR2020+ system architecture and design such that their emergent behaviours become positive. This objective is accomplished by exploiting innovative complexity science techniques that have been developed during the last ten years through large European Commission research projects HYBRIDGE (2002-2005) and iFly (2007-2011).