Complexity Measurements And Causation For Dynamic Complex Systems
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Complexity Measurements and Causation for Dynamic Complex Systems
Author: Juan Guillermo Diaz Ochoa
language: en
Publisher: Springer Nature
Release Date: 2025-03-13
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.
Dynamics Of Complex Systems
This book aims to develop models and modeling techniques that are useful when applied to all complex systems. It adopts both analytic tools and computer simulation. The book is intended for students and researchers with a variety of backgrounds.
Algorithmic Information Dynamics
Author: Hector Zenil
language: en
Publisher: Cambridge University Press
Release Date: 2023-05-25
Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.