Contributions
- We have shown 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 online]
- 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, online and online].
- How natural selection may harness computation and how algorithmic dynamics can explain aspects of biological evolution and , such as speed of convergence, natural mass extinctions and diversity explosions, among others. By taking organisms moving in software space we have always defined a concept of back mutations, this is mutations that are algorithmically more likely to occur because they are in the computationally evolutionary pathway of the system. [online]
- How our indexes can help in challenges of structural biology such as nucleosome occupancy/positioning. [online]
- 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.
We have created these videos to help explain our research interests and contributions:
|
|
|
These videos do not, of course, contain all the details and information that only reading the full papers can provide.
And a teaser of an online course (MOOC) in preparation to come
out in the Sta Fe Institute's Complexity Explorer platform:
out in the Sta Fe Institute's Complexity Explorer platform:
