Sophocles · Self-Organised information PrOcessing, CriticaLity and Emergence in multilevel Systems

We will contribute to a theory of dynamics of multi level complex systems by developing mathematical and computational formalisms for information processing in such multi level systems. We will develop the formalism in the context of criticality, emergence, and tipping points in multi level systems and apply it to real data. This should lead to a better understanding, but more important, to an improvement in predictive power for early warning. Can we observe tell-tales of things to happen in the (near) future? We will relate the emergence of structures and collective effects to the existence of an information-driven phase transition. Emergent structures may mean selection of preferred scales, creation of new levels or annihilation of existing levels, or occurrence of tipping points leading to extreme phenomena. We believe that these transitions are often self-organized because they appear in a spontaneous way, driven only by the dynamics of the system and the co-evolving topology of the interactions. We will create an experimental facility, a Computational Exploratory, which allows to implement our theoretical framework of information processing in multilevel complex systems, and to apply this to real life data. The theory will be validated on real world applications involving large, heterogeneous multi level datasets from the Socio-Economic domain (high frequency FX data, datasets on interest rates, and social media data) and applied to study the question of emergence of scales, and the detection and prediction of tipping points in real-life datasets. We contribute to the questions if and why Nature has preferred scales, and if so, if such emerging scales can be detected in real data sets. The impact of our theory on understanding of emergence of multilevel systems due to critical information processing is expected to be substantial. Our theory will offer new tools for critical transitions and extreme events prediction in real-life datasets.