Almost all interesting processes in nature and society are highly cross linked. In many systems, however, we can distinguish a set of fundamental building blocks, which interact nonlinearly to form compound structures or functions with an identity that requires more explanatory devices than those used to explain the building blocks.
Multivariate systems that need complementary, multi-level modes of description are defined as complex systems. They are typically modelled as networks or dynamical systems.
The lab is particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve. This means both understanding how information is fundamental for controlling the behavior and evolutionary capabilities of complex systems, as well as abstracting principles from natural systems to produce adaptive information technology.
This theoretical and applied research agenda is organized in three main threads:
Please also check the research group on Complex Adaptive Systems and Computational Intelligence (CASCI), for more details about our research and how to collaborate and study with us. The research group is also committed to interdisciplinary research, teaching and graduate training.
Networks are mathematical objects used to study multivariate systems.
Perhaps the most active interdisciplinary research arena is the intersection of the life sciences with informatics.
A key problem is how information, symbols and the like can arise from a purely dynamical system of many components.
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