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This book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems autonomy and variability for causal inference i.e., the ability to derive causal relationships from data recorded as a function of time. Such complexity measures present limitations in the derivation of absolute causality in complex systems and the recognition of relative and contextual causality, with practical consequences for causal inference and modeling.
Finally, the author provides concepts for relative causal determinism. As a result, new ideas are presented to explore the frontiers of systems theory, specifically in relation to biological systems and teleonomy, i.e., evolved biological purposiveness.
This book is written for graduate students in physics, biology, medicine, social sciences, economics, and engineering who are seeking new concepts of causal inference applied in systems theory. It is also intended for scientists with an interest in philosophy and philosophers interested in the foundations of systems theory. Additionally, data scientists seeking new methods for the analysis of time series to extract features useful for machine learning will find this book of interest.
List of contents
Concepts of Causality and Systems theory.- A brief overview on Dynamic Complex Systems And Causal Inference.- Elastic States and Complex Dynamics in Mechanistic Models.- A cartography of complexity.- The implications of relative causal inference for the understanding of complex systems.
About the author
Juan G. Diaz Ochoa is a physicist and entrepreneur with an interest in the fundamental aspects of complex systems, philosophy and innovation. Aside from studying astrophysics at the National Observatory of Colombia (rotating neutron stars in non-Newtonian approximations), Juan G. Diaz Ochoa obtained his PhD at the University of Mainz in theoretical physics and condensed matter, specifically molecular dynamics of polymer melts, where he completed his doctorate in physics within Professor Binder's group.
After different stations, including the Max Planck Institute for complex technical systems, he moved to industry, worked with different companies and founded his own enterprises in the field of biomedicine. He is also a lecturer in mathematics and algorithms and complexity at DHBW in Stuttgart and a member of various societies, including the German Physical Society and DECHEMA.
In recent years, he has explored fundamental questions regarding systems theory and biology and has made efforts to develop mathematical methods applied to various complex systems, from condensed matter to biomedicine. He has been involved in the development of novel mathematical concepts and artificial intelligence algorithms applied to medicine, with a focus on transparent artificial intelligence architectures, graph knowledge, topological machine learning and language models. He is also actively working on ethical issues in machine learning and artificial intelligence and coedited an article collection on transparent machine learning in medicine.
Summary
This book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems’ autonomy and variability for causal inference—i.e., the ability to derive causal relationships from data recorded as a function of time. Such complexity measures present limitations in the derivation of absolute causality in complex systems and the recognition of relative and contextual causality, with practical consequences for causal inference and modeling.
Finally, the author provides concepts for relative causal determinism. As a result, new ideas are presented to explore the frontiers of systems theory, specifically in relation to biological systems and teleonomy, i.e., evolved biological purposiveness.
This book is written for graduate students in physics, biology, medicine, social sciences, economics, and engineering who are seeking new concepts of causal inference applied in systems theory. It is also intended for scientists with an interest in philosophy and philosophers interested in the foundations of systems theory. Additionally, data scientists seeking new methods for the analysis of time series to extract features useful for machine learning will find this book of interest.
