Information Theory for Complex Systems: An Information Perspective on Complexity in Dynamical Systems and Statistical Mechanics

This book introduces a comprehensive framework tailored for dissecting complex systems across diverse disciplines. What defines a complex system? How can we harness information to quantify its order, structure, and intricacy? Delving into phenomena from the intricate processes in physical systems to the dynamic behaviours in cellular automata and pattern formation, readers will uncover the profound interplay between physics and information theory. This intricate relationship provides fresh insight into physical phenomena, reimagining them through the lens of information. Notably, the book demystifies how seemingly opposing forces-rising order and increasing disorder-coexist, ultimately shedding light on the second law of thermodynamics as an outcome of deterministic, reversible dynamics beneath the surface. Geared towards graduate students, this book presumes an undergraduate foundation in mathematics and physics, ensuring a deep, engaging exploration for its readers.


Author: Kristian Lindgren
Publisher: Springer
Published: 01/05/2024
Pages: 153
Binding Type: Hardcover
Weight: 0.91lbs
Size: 9.21h x 6.14w x 0.44d
ISBN: 9783662682128

About the Author
Kristian Lindgren is a Professor Emeritus in Complex Systems at Chalmers University of Technology. Originating from a background in physics, he has applied this foundational knowledge to the interdisciplinary study of complex systems across a variety of disciplines. His key research areas include information theory and statistical mechanics, as well as the application of information theory to complex and self-organizing systems. Lindgren has also delved into game theory as it applies to evolutionary systems and has utilized agent-based modelling to understand economic systems. He was one of the founders of the International Masters Programme in Complex Adaptive Systems at Chalmers in 2000 and has also served as the Director for the Graduate School (Ph.D.) in Complex Systems.