To this end, we have created  research teams each tacking different fundamental questions, one is devoted to introducing cause and effect in the practice of data analytics and the introduction of model-based reasoning complementing and enabling current statistical machine learning approaches (including deep learning) to better deal with questions of causation. Related to this, a major challenge is how to combine the power of symbolic computation in its fundamentally discrete form with the power of essentially continuous fields such as differentiable programming and dynamical systems.

Our aim is to introduce and exploit the most powerful mathematical theories bringing to bear mature concepts (such as multi-scale dynamics, spectral theory, and algorithmic inference) in solving some of the most pressing problems in the areas of systems modelling.
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Our logo epitomizes the type of phenomena we aim to help disentangle. The graph is described in paper J24 and its generating mechanism is a very simple, recursive short computer program, yet it produces a network with complex graph-theoretic properties. The graph also displays maximal Shannon entropy when described by its degree sequence and measures by an uninformed observer but minimal Shannon entropy when looking at its adjacency matrixthereby showing the failure of statistical mechanics approaches and of computable measures in general such as Entropy to characterize causal non-random macroscopic systems.

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​On the other hand, the Algorithmic Nature grouphas developed some of the main numerical methods at the core of the conceptual framework that is fuelling our pursuit of a better understanding of the causal mechanisms and first design principles of natural and artificial evolving systems, including a sophisticated online calculator that provides estimations of algorithmic complexity and logical depth by way of the powerful concept of algorithmic probability that combines traditional probability with computation. We continue to foster and nurture connections between our sister labs in order to further the development of such numerical and conceptual methods as well as to explore aspects of animal and human behaviour from this algorithmic and powerful perspective (see paper J23).