Information and self-organization : a macroscopic approach to complex systems
著者
書誌事項
Information and self-organization : a macroscopic approach to complex systems
（Springer series in synergetics）
New York : Springer, c2000
2nd enl. ed
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注記
Includes bibliographical references (p. [211]-216) and index
内容説明・目次
内容説明
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.
目次
The Challenge of Complex Systems * From the Microscopic to the Macroscopic World ... * ... and Back Again: The Maximum Information Principle (MIP) * An Example from Physics: Thermodynamics * Application of the Maximum Information Principle to Self-Organizing Systems * The Maximum Information Principle for Nonequilibrium Phase Transitions: Determination of Order Parameters, Enslaved Modes, and Emerging Patterns * Information, Information Gain, and Efficiency of Self-Organizing Systems Close to Their Instability Points * Direct Determination of Lagrange Multipliers * Unbiased Modeling of Stochastic Processes: How to Guess Path Integrals, Fokker-Planck Equations and Langevin lto Equations * Application to Some Physical Systems * Transitions Between Behavioral Patterns in Biology. An Example: Hand Movements * Pattern Recognition * Quantum Systems * Concluding Remarks and Outlook * References * Subject Index.
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