DOI
https://doi.org/10.1201/9780429455049

Summary
Complex Systems are natural systems that science is unable to describe exhaustively. Examples of Complex Systems are both unicellular and multicellular living beings; human brains; human immune systems; ecosystems; human societies; the global economy; the climate and geology of our planet. This book is an account of a marvelous interdisciplinary journey the author made to understand properties of the Complex Systems. He has undertaken his trip, equipped with the fundamental principles of physical chemistry, in particular, the Second Law of Thermodynamics that describes the spontaneous evolution of our universe, and the tools of Non-linear dynamics. By dealing with many disciplines, in particular, chemistry, biology, physics, economy, and philosophy, the author demonstrates that Complex Systems are intertwined networks, working in out-of-equilibrium conditions, which exhibit emergent properties, such as self-organization phenomena and chaotic behaviors in time and space.

Relevance for Complex Systems Knowledge
This book presents a definition of Natural Complexity based on the features shared by all Complex Systems. Furthermore, it explains why science cannot describe Complex Systems exhaustively. In other words, why science cannot predict Complex Systems' behavior, especially in the long term. There are three principal reasons. The first reason is linked to Computational Complexity: many computational problems regarding Complex Systems are solvable but intractable. The second reason is that Complex Systems exhibit variable patterns. There are not universally valid and effective algorithms for recognizing variable patterns. Finally, the third reason is that the predictive power of science has intrinsic limitations. These limitations regard the dynamics of microscopic particles and the microscopic and macroscopic world's chaotic dynamics.

Point of Strength
This book traces a path to follow to deepen our knowledge about Natural Complexity. It incites to develop Natural Computing that is an interdisciplinary research line drawing inspiration from nature to propose: (I) new algorithms; (II) new materials and architectures to compute; (III) new methodologies and models to interpret Natural Complexity. The rationale is that every natural transformation can be conceived as a computation, because any distinguishable physicochemical state of matter and energy can be used to encode information.