It comes as something ofa surprise to reflect that the New York Review ofBooks is now over twenty years old. Even people of my generation (that is, old enough to remember the revolutionary 196os but not young enough to have taken a very exciting part in them) think of the paper as eternally youthful. In fact, it has gone through years of relatively quiet life, yet, as always in a competitive journalistic market, it is the controversies that attract the interest of the reader and to which the history (especially an admittedly impressionistic survey that tries to include something of the intellectual context in which a journal has operated) must give some attention. Not all the attacks which the New York Review has attracted, both early in its career and more recently, are worth more than a brief summary. What do we now make, for example, of Richard Kostelanetz's forthright accusation that 'The New York Review was from its origins destined to publicize Random House's (and especially [Jason] Epstein's) books and writers'?1 Well, simply that, even if the statistics bear out the charge (and Kostelanetz provides some suggestive evidence to support it, at least with respect to some early issues), there is nothing surprising in a market economy about a publisher trying to push his books through the pages of a journal edited by his friends. True, the New York Review has not had room to review more than around fifteen books in each issue and there could be a bias in the selection of

http://www.jstor.org/stable/pdfplus/3507774.pdf

The New York Review of Books

Economics and Information Theory

System Identification I

The Theory of Stochastic Processes. By D. R. Cox and H. D. Miller. Pp. x, 398. 70s. (Methuen)

Mathematical Handbook for Scientists and Engineers

Preface. Acknowledgments. Introduction. 1. Mathematical Foundations of Informational Macrodynamics. 1.1 Information Variational Principle. 1.2 The Space Distributed Macromodel. 1.3 The optimal time-space distributed macromodel with consolidated states (OPMC). 2. Information Systems Modeling. 2.1 Informations systems theory and IMD. 2.2 Some General Information Macrosystemic Functions. 3. Information-Physical Models and Analogies. 4. Solution of the Applied IMD problems. 4.1 Analytical and Numerical Solutions. 4.2 Data Modeling and Communications. 4.3Cognitive Modeling in Artificial Intelligence. 4.4 The Information Network for Performance Evaluation in Education. 4.5 The Information Dynamic Model of Macroeconomics. 4.6 The Information Macromodels in Biology and Medicine. 4.7 Industrial Technology's IMD Applications and Implementations. Conclusion. Index.

Information Systems Analysis and Modeling: An Informational Macrodynamics Approach

An observer's information dynamics: Acquisition of information and the origin of the cognitive dynamics

Solution to the variation problem for information path functional of a controlled random process

The boundary value problem and the Jensen inequality for an entropy functional of a Markov diffusion process

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

Vladimir S. Lerner

Papers

Information Systems Analysis and Modeling: An Informational Macrodynamics Approach

An observer's information dynamics: Acquisition of information and the origin of the cognitive dynamics

Solution to the variation problem for information path functional of a controlled random process

The boundary value problem and the Jensen inequality for an entropy functional of a Markov diffusion process

Variation Principle in Informational Macrodynamics