[CANCELLED] Seminar "A data-driven approach to identify synergistic and emergent phenomena in multivariate time series"

This event has been cancelled due to changes in the host's schedule. We apologise for the inconvenience.

Abstract:

The concepts of synergy and emergence lay at the core of Complexity Science, being so much a cause of wonder as a perennial source of philosophical headaches. Part of the difficulty in deepening our understanding on these subjects lies in the absence of analytical models and clear metrics that could serve the community to guide discussions and mature theories. In this talk we present practically useful and ontologically innocent tools to measure synergistic and emergent phenomena from multivariate data, and illustrate them with some selected case studies.

The first part of the talk is devoted to synergistic phenomena, which is considered as statistical regularities that affect the whole but not the parts at a given moment — extending the traditional notion of synchronization. We show how synergistic phenomena can be captured by various information-theoretic metrics, and explore non-trivial connections with the data privacy literature. In the second part of the talk we show how emergent phenomena can be assessed by Integrated Information Decomposition (ΦID), which provides a taxonomy of information dynamics phenomena. Furthermore, we show how ΦID provides practical criteria that is applicable in a range of scenarios of practical interest. We illustrate the various metrics with different case studies, including cellular automata, baroque music, and neuroimaging datasets.

References:

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.032305

https://arxiv.org/abs/1909.02297

https://www.mdpi.com/1099-4300/20/10/793

Personal info:

Name: Fernando E. Rosas

Affiliation: 

Data Science Institute, Imperial College London. 

Centre for Psychedelic Research, Department of Brain Science, Imperial College London. 

Centre for complexity Science, Imperial College London