"In the theoretical aspects, we developed a new framework to study multivariate correlations in systems described by multiple variables. Our framework contain various aspects:
- New notions of how information can be stored in multi-agent systems: We develop an information-theoretic framework to quantify various modes of information storage that can take place in a multi-agent system. Moreover, we shown how these modes can be useful to characterise various phases in self-organisation, and can provide useful characterisation of information-processing capabilities of complex systems.
- Generalized metrics of statistical synergy: We developed a new metric, called ""O-information"", which can be used to assess to what extend high-order interdependencies (i.e. ones that involve many variables, but cannot be reduced to interaction between smaller numbers of variables) dominate over pairwise or other low-order interdependencies. Our framework can be used for characterising fundamental organizational properties in a data-driven model-free fashion. With this framework we were able to show surprising properties of the statistics of the scores of J.S. Bach, which are not present in the music of some of his contemporaries.
- New notions of information dynamics. Traditional causal discovery frameworks only consider single 'cause'-variables that act on single 'effect'-variables. We extend this view developing a framework that considers high-order causes, which furthermore might act on high-order effects. With this, we developed a fine taxonomy of information dynamics phenomena, which can be used to disentangle dissimilar phenomena that is conflated by popular metrics such as transfer entropy.
In the field of applications of our theories, our results include the following areas:
- With respect to data privacy, we developed a new paradigm to disclose data which keeping critical contents secure. In particular, our approach proposes to disclose collective properties of databases which keep the actual value of each data-sample perfectly private. For this, we developed practical algorithms, and studied asymptotic performance limits.
- In computational neuroscience, we showed that the brain activity of musicians show a higher complexity when they improvise. High brain complexity has been associated with high state of alertness and awareness. Interestingly, some audience members also show this increase, although the effect is not so consistent.
- In neuroscience of psychedelics, we found that brain complexity also increases systematically with the richness of stimuli. Moreover, brain complexity also increases with the usage of LSD; interestingly, after LSD has been consumed, the outside stimuli still increase the complexity but the effect decreases. This contributes to the better understanding of the clinical usage of psychedelics.
- In computational social sciences, we shows that social learning algorithms can be used to guarantee network robustness against data falsification attacks. Additionally, we showed how social diversity can introduce stability and better network long-term network performance.
Our work with music improvisation received plenty of media coverage, being featured in The Times and BBC. Additionally, we made two concert+talk activities: one at the Imperial's Late Festival (06-dec-2018), and one in the Guildhall School of Music (01-may-2019). We have scheduled a new concert+talk that will take in the Exhibition Road Festival (30-june-2019). We also disseminated our results in the workshop ""The power of musical networks"", co-organised with the Orpheus Institute, which took place in Gent, Belgium (21/22-feb-2018).
The results of the project was presented at multiple international conferences and meetings, including CompNet2017, CCS2017 and 2018, NetSci2018, and Worshops at Oxford, Amsterdamn, Hong-Kong, Enschede, Gent, Valparaiso and Cuernavaca."
