Synopses & Reviews
Informational Macrodynamics (IMD) combines both the informationdescription of the interacted information flows initiated by differentdata sources, and the information systemic approach, which unify thedistinct interdisciplinary modeling concepts for a variety of objectsof different natures. The IMD formalism builds a bridge between themathematical modeling and systemic formalism with the world ofinformation and information technologies to reveal the commoninformation regularities of a variety of modeling objects with a finalgoal to expose a specific information code for each object.The Variation Principle In Informational Macrodynamics (VP) introducesthe process' integral information measure, which has a distinctivedifference from traditional information approaches that use an entropyfunction. The VP's minimax mathematical mechanisms formalize theregularities of the cooperative informational dynamics, connectingrandomness and regularities, stochastic and determinism, reversibilityand irreversibility, symmetry and nonsymmetry, stability andinstability, regular and chaotic dynamics, thermodynamics andinformational dynamics, time-reversible and time-irreversibleprocesses, reveals the information dynamic mechanisms of evolution anddevelopment.The mathematical formalisms and methods, implemented in the forms ofcomputer models, algorithms and programs, provide new general toolsfor Information Systems' Modeling in such areas as biology, intelligent systems, computer and information technology, includingcommunications, data modeling and data management."Variation Principle In Informational Macrodynamics," with itsexamples and applications, will meet the needs of a professionalaudience composedof researchers and practitioners in industry, aswell as graduate-level students in computer science, mathematics andengineering.
Synopsis
Information Macrodynamics (IMD) belong to an interdisciplinary science that represents a new theoretical and computer-based methodology for a system informational descriptionand improvement, including various activities in such areas as thinking, intelligent processes, communications, management, and other nonphysical subjects with their mutual interactions, informational superimposition, and theinformation transferredbetweeninteractions. The IMD is based on the implementation of a single concept by a unique mathematical principle and formalism, rather than on an artificial combination of many arbitrary, auxiliary concepts and/or postulates and different mathematical subjects, such as the game, automata, catastrophe, logical operations theories, etc. This concept is explored mathematically using classical mathematics as calculus of variation and the probability theory, which are potent enough, without needing to developnew, specifiedmathematical systemicmethods. The formal IMD model automatically includes the related results from other fields, such as linear, nonlinear, collective and chaotic dynamics, stability theory, theory of information, physical analogies of classical and quantum mechanics, irreversible thermodynamics, andkinetics. The main IMD goal is to reveal the information regularities, mathematically expressed by the considered variation principle (VP), as a mathematical tool to extractthe regularities and define the model, whichdescribes theregularities. The IMD regularities and mechanisms are the results of the analytical solutions and are not retained by logical argumentation, rational introduction, and a reasonable discussion. The IMD's information computer modeling formalism includes a human being (as an observer, carrier and producer ofinformation), with a restoration of the model during the objectobservation
Synopsis
The Variation Principle In Informational Macrodynamics (VP) introduces the process' integral information measure, which has a distinctive difference from traditional information approaches that use an entropy function. The VP's minimax mathematical mechanisms formalize the regularities of the cooperative informational dynamics, connecting randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and nonsymmetry, stability and instability, regular and chaotic dynamics, thermodynamics and informational dynamics, time-reversible and time-irreversible processes, reveals the information dynamic mechanisms of evolution and development. The mathematical formalisms and methods, implemented in the forms of computer models, algorithms and programs, provide new general tools for Information Systems' Modeling in such areas as biology, intelligent systems, computer and information technology, including communications, data modeling and data management. Variation Principle In Informational Macrodynamics, with its examples and applications, will meet the needs of a professional audience composed of researchers and practitioners in industry, as well as graduate-level students in computer science, mathematics and engineering.
Table of Contents
Preface. I: The IMD Essence And Concepts. 1. Introduction. 1.1. Notion of Information. 1.2. Information Modeling. 2. The Information Modeling Concepts. 2.1. Initial Statements and Starting Points. 2.2. The Modeling Mechanism. 2.3. The Macromodel's Structure and Organization. 2.4. The Model's Controls, Joint Optimal Synthesis and Model's Identification. 2.5. The Model's Chaotic and Quantum Phenomena. 2.6. Region of Uncertainty and Systemic Invariants. 2.7. Compression of the Incoming Information. 2.8. The Evaluation of Information Contributions into the IN's Structure. 2.9. The Optimal Code's Language. 2.10. An Initial Triplet as a Carrier of the Total Macrostructure's Genetic Information. 2.11. Macrosystemic Complexity. 3. General Macrosystemic Functions. 3.1. Systemic Generalizations. 3.2. Mutation, Diversity, and Adaptation. 3.3. The Macroprocess' Evolution. 3.4. Robustness, Selection, Competition, Cooperation, and Self-Organization. 3.5. The Transformation of Imaginary into Real Information, Connection to Quantum Mechanics and Evolution. 3.6. Information Structure of the Control Mechanisms of the Cyclic Evolution. 3.7. Mechanism of Assembling the Node's Frequencies and Automatic Selection. 3.8. The Cyclic Model's Information Mechanisms. 3.9. Examples of the DSS' codes. 3.10. An Evaluation of Maximum Information Delivered from Environment. 3.11. About a Life-Time Duration of the IMD Model. 4. Main Macrosystemic Equations and Information Analogies. 4.1. Marcovian Processes and Equations of Math Physics. 4.1a. Examples of Extremal Principles. 4.2. An Analogy with the Feynman Path Functional in Quantum Mechanics. 4.3. Minimax Principle. 4.4. Macrolevel Dynamics. 4.5. Information Mass. 4.6. Information Forces. 4.7. The Information Virtual and Physical Connections. 4.8. The Invariant Transformation of the Model's Eigenvalues. 4.9. The Informational Analogies of Physical Invariants. 4.10. The Bound Energy of Information Cooperation. 5. The IMD's Relations to the Fundamental Sciences. 5.1. Classical Mechanics. 5.2. Special Theory of Relativity (TR). 5.3. Quantum and Statistical Mechanics. 5.4. Gravitation Theory. 5.5. String Theory. 5.6. Theory of Phase Transformations. 5.7. Theory of Stability. 5.8. Dynamic Systems Theory and Kolmogorov's Complexity. 5.9. Chaotic Dynamics. 5.10. Nonequilibrium Thermodynamics (NT). 5.10a. The NT and IMD Connections: Evolution Process of the Earth. 5.11. Statistical Physics and Equilibrium Thermodynamics. 5.13. General and Information Science. References. II: Mathematical Foundation Of Informational Macrodynamics. 1. Variation Problem for Dynamic Informational Modeling of Random Process. 1.1. Initial Mathematical Models and Statements. 1.2. The Probabilistic Evaluation of Micro-and Macrolevel's Processes. 1.3. Solution of the Variation Problem. References. 2. The Space Distributed Macromodel. 2.1. The Information Macrofunctional and the Euler-Ostrogradsky Equations. 2.2. The Invariant Conditions at the Transformation of Space Coordinates. 2.3. The Parameters of the Space Transformation and the Distributed Macromodels. 2.4. The Time-Space Movement Toward the Macromodel's Cooperation. 2.5. Starting the Real Time's Macroprocess. References. 3. The Optimal Time-Space Distributed Macromodel (OPMC) with the Consolidated States. 3.1. Local Invariants and Dynamic Peculiarities. 3.2. The OPMC Geometrical Structure. 3.3. The Triplet's Structure. 3.4. The OPMC Classification and Accuracy. 3.5. The Equations of an Arbitrary IN's Elementary Triplet. References. III: Applications. 1. Solution of the Applied Problems on Examples. 1.1. The Identification of Concentrated Object's Model. 1.2. The Identification of the Space Distributed Object's Model. 1.3. Synthesis of the Optimal Control. 1.4. The Procedure of Joint Identification, Optimal Control, and Consolidation. 1.5. The Decision Making Processes in the Utility Theory. 2. A Review of the Main Applications. 2.1. Intelligence Systems. 2.2. Macroeconomics. 2.3. Biological Systems. 2.4. Summary of Other Practical Applications. Conclusion. References. Index.