Item description for Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer Series in Synergetics) by Hermann Haken...
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, life and computer science. The relationship to chaos theory is examined and it is further shown that, 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 allows for probabilistic predictions of processes, with applications to numerous fields in science, technology, medicine and economics. The extensions of the third edition are essentially devoted to an introduction to the meaning of information in the quantum context. Indeed, quantum information science and technology is presently one of the most active fields of research at the interface of physics, technology and information sciences and has already established itself as one of the major future technologies for processing and communicating information on any scale.
This book addresses graduate students and nonspecialist researchers wishing to get acquainted with the concept of information from a scientific perspective in more depth. It is suitable as a textbook for advanced courses or for self-study.
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Est. Packaging Dimensions: Length: 9.52" Width: 6.4" Height: 0.68" Weight: 0.94 lbs.
Release Date Jan 7, 2000
ISBN 3540662863 ISBN13 9783540662860
Reviews - What do customers think about Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer Series in Synergetics)?
physics approach Sep 1, 2000
Self-Organization is not the function given to a neural net (although they have taken it) used for pattern recognition, nor is it a cult somewhere in Germany. After following Haken's work for 6-8 years it is good to see a summary of sorts. Haken was working with self-organizing similarities in the 80's when unification ideas were rampant. Haken uses this same analogy by equating the basic form to stochastic differential equations. It is somewhat easier to approach the differential equation as a dynamical system driven by random vector fields of which the Ito form (stuff Kalman filters are made of) is a special case. Without going into martingales Brownian motion ergodic theorems of Markovian processes Haken does give a convincing argument for what he terms MIP (max. information principle) and information gain in the system. Linguistically converted this means that the process may be likened to a diffusion process with thermodynamic stuff. This paves the way for the transfer of information from one organization structure diffusion (in the wave) front to another. It seems to me, however, that a much simpler proof would be; show the parallel between Haken's basic form and the Lax form of an evolution equation. Establish relationship to Hirota's derivatives. Usually represented and manifested as the Korteweg-deVrie equations the polynomials groups describing the equation easily convert to Hiroto derivatives. Show fundamental relationship to n-solitons and vertex operators, establish relationship to Heisenberg and Clifford algebras, show Fock representation of Bosons using Maya diagrams, show Boson-Fermion correspondence. Complex variables, infinite dimensional algebras, Fermions, and Bosons; The principle of superposition does not apply to non-linear waves, despite that there exists exact solutions containing an arbitrary number of parameters suggesting an infinite dimensional transformation group acting on spaces of solutions of integrable systems (Reaction-diffusion as one type shock waves as another). Because of this self-symmetry in scales of complex polynomials, transformational methods work well. If waves are information densities and an increase in entropy is an increase of information Hiroto's derivatives would give the mathematical link showing the degrees of information transfer between types of diffusion front (waves) and another. The similarity of scales, the repeating nature, then transfer of one wave front (diffusion) through another without annihilation.