The central aim of HIERATIC is to develop a new framework for understanding complex systems as a multi-level hierarchy of sub-systems using non-linear decompositions. To achieve this goal, HIERATIC is structured in three interlinked sets of activities: theoretical work, deriving the novel mathematics required to identify suitable non-linear state space reductions of complex systems software development of efficient multi-scale simulation and prediction libraries demonstrators, illustrating the power of our results network dynamics, cell cycle simulations, social interactions in animals. The theoretical work will use unconventional approaches from topology and dynamical systems theory to derive an algorithmic approach to identifying coarse-grainings of large complex systems. These algorithms will be used to develop highly efficient simulation and prediction tools, integrated with the world-leading software libraries MASON and PRISM. The demonstrators will show the potential application of these techniques, in a range of applications, including validation on large empirical data sets. The project brings together leading researchers in complex systems theory, biosystems, multi-agent simulation, and experimental ecology, from around the EU and USA.
Prefekt at Energy and Environment, Physical Resource Theory
Birmingham, United Kingdom
Sheffield, United Kingdom
Funding years 2012–2015
Chalmers Driving Force