Name: Complexity Measurements and Causation for Dynamic Complex Systems (Understanding Complex Systems) (en Inglés)
SKU: 64206233
Price: 63.05 USD
Availability: InStock
Author: Juan Guillermo Diaz Ochoa
ISBN: 9783031847080

portada Complexity Measurements and Causation for Dynamic Complex Systems (Understanding Complex Systems) (en Inglés)

Complexity Measurements and Causation for Dynamic Complex Systems (Understanding Complex Systems) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Juan Guillermo Diaz Ochoa

Idioma

Inglés

N° páginas

159

Encuadernación

Tapa Dura

ISBN13

9783031847080

Complexity Measurements and Causation for Dynamic Complex Systems (Understanding Complex Systems) (en Inglés)

Juan Guillermo Diaz Ochoa (Autor) · Springer · Tapa Dura

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Reseña del libro "Complexity Measurements and Causation for Dynamic Complex Systems (Understanding Complex Systems) (en Inglés)"

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.