Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer Series in Synergetics)

This book presents the concepts needed to deal with self-organizing complex systems from a unifying point of view that uses macroscopic data. The various meanings of the concept "information" are discussed and a general formulation of the maximum information (entropy) principle is used. With the aid of results from synergetics, adequate objective constraints for a large class of self-organizing systems are formulated and examples are given from physics, biology, and computer science (pattern recognition by parallel computers). The extensions contained in the second edition show how, based on possibly scarce and noisy data, unbiased guesses about processes of complex systems can be made and the underlying deterministic and random forces determined. This procedure allows probabilistic predictions of processes, with applications to numerous fields ranging from technology through biology and medicine to economy. The relationship to chaos theory is also addressed.

From the reviews of the third edition:

"This enlarged edition of Information and Self-Organization addresses the concept of information in depth: ranging ‘from Shannon information, from which all semantics has been exorcised, to the effects of information on receivers and the self-creation of meaning’―that is, toward semantic information ... . Nevertheless, both the qualitative lessons and quantitative analysis presented in the book ... very useful for artificial life researchers." (Mikhail Prokopenko, Artificial Life, Vol. 15, 2009)