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| Author(s): |
Tiago Martinelli
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Física de São Carlos (IFSC/BT) |
| Defense date: | 2023-04-20 |
| Examining board members: |
Francisco Aparecido Rodrigues;
Zhao Liang;
Pedro Antonio Martinez Mediano;
Thomas Kauê Dal'Maso Peron;
Gonzalo Travieso
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| Advisor: | Francisco Aparecido Rodrigues; Diogo de Oliveira Soares Pinto |
| Abstract | |
The big data era advanced the possibility of studying emergent phenomena in the real world, often occurring by systems with high-order, non-trivial interactions. One of the main questions for these complex systems is to understand how their collective organization influences the dynamic processes. Although such a study is fundamental to developing the policies of controlling dynamical processes from changes in the network structure, in practice, the only information available is multivariate data recorded from variables with unknown topology. Such a scenario can be explored using information theory and causality tools to quantify an individuals influence and infer a causal structure among them. In other words, we can make reverse engineering to obtain a causal model via data. However, a methodology to deal with emergent causes when extracting information is an open question. If not performed correctly, it can compromise basic assumptions in causal modeling resulting in a spurious view of the organization of complex systems. This thesis is dedicated to investigating fundamental problems regarding the capture of emergence phenomena from high-order complex systems joining techniques from causal manipulative approaches and multivariate information theory. Based on our results, we defend a paradigm shift when dealing with multivariate data in causal modeling by considering the task of a system description by moving scales as a fundamental issue instead of a mathematical artifice. (AU) | |
| FAPESP's process: | 18/12072-0 - Reconstruction of complex networks from causal information |
| Grantee: | Tiago Martinelli |
| Support Opportunities: | Scholarships in Brazil - Doctorate |