We have shown so far
- That indexes based on Shannon Entropy cannot be considered robust measures of randomness especially in the context of maximal entropy models [see J25 and P12].
- That computability, causality and dynamical systems are deeply connected by way of algorithmic probability [J, 22, P7, J4].
- That ranking, clustering, reconstruction and dimension-reductionality methods based upon principles at the intersection of dynamical systems, computability and algorithmic information are highly informative of causal generating mechanisms and first principles of causal systems. [see J16, J15, J22, P10, J9, P8, J17, J1 and new forthcoming]
- A minimal mathematical model for Multiple Sclerosis that displays the most important stages and dynamic features of the disease [J24].
- That novel methods can steer and reprogram networks and systems, both biological and synthetic. [see J17, P11, J4, preprint and new forthcoming].
- We also introduced numerical improvements and a precise calculation pipeline to infer and reverse engineer networks based on adaptive differentiation [see paper J27].
We aim at connecting computation, algorithmic information and dynamical systems to reveal the causal content and generating mechanisms and models of synthetic and biological systems.
For more information, visit Publications.
Soon you will also find here some animated videos showing these results in a visual fashion.