Complex and Adaptive Dynamical Systems

This textbook offers a comprehensive introduction to the concepts underpinning our modern understanding of complex and emergent behavior. Mathematical methods necessary for the discussion are introduced and explained on the run. All derivations are presented step-by-step. This new fifth edition has been fully revised and includes a new chapter, a range of new sections, figures and exercises. The Solution chapter has been reorganized for clarity.

The core aspects of modern complex system sciences are presented in the first chapters, covering the foundations of network- and dynamical system theory, with a particular focus on scale-free networks and tipping phenomena. The notion of deterministic chaos is treated together with bifurcation theory and the intricacies of time delays. Modern information theoretical principles are discussed in further chapters, together with the notion of self-organized criticality, synchronization phenomena, and a game-theoretical treatment of the tragedy of the commons. The dynamical systems view of modern machine learning is presented in a new chapter.

Chapters include exercises and suggestions for further reading. The textbook is suitable for graduate and advanced undergraduate students. The prerequisites are the basic mathematical tools of courses in natural sciences, computer science or engineering.

Suitable for the study of complex systems theory, teaching purposes, and as a reference Fully revised 5th edition, now featuring a new chapter on machine learning Expanded solutions available for all end-of-chapter exercises

Autorentext

Professor Claudius Gros currently holds a chair in theoretical physics at Johann Wolfgang von Goethe University in Frankfurt am Main, Germany. He completed his PhD in theoretical condensed-matter physics in 1988 at ETH in Zürich, Switzerland. Afterward, Prof. Gros served as the Chester-Davies fellow at Indiana University, US, before returning to Germany. His current research interests revolve around complex systems theory, including self-organized robotics and the physics of AI.

Inhalt

Network Theory.- Bifurcations and Chaos in Dynamical Systems.- Dissipation, Noise and Adaptive Systems.- Self Organization.- Information Theory of Complex Systems.- Self-Organized Criticality.- Random Boolean Networks.- Darwinian Evolution, Hypercycles and Game Theory.- Synchronization Phenomena.- Complexity of Machine Learning.- Solutions.