This paper proposes a paradigm for managing the dynamic aspects of organizational knowledge creating processes. Its central theme is that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge. The nature of this dialogue is examined and four patterns of interaction involving tacit and explicit knowledge are identified. It is argued that while new knowledge is developed by individuals, organizations play a critical role in articulating and amplifying that knowledge. A theoretical framework is developed which provides an analytical perspective on the constituent dimensions of knowledge creation. This framework is then applied in two operational models for facilitating the dynamic creation of appropriate organizational knowledge.

/pdf/a-dynamic-theory-of-organizational-knowledge-creation-22t9dxu55o.pdf

A Dynamic Theory of Organizational Knowledge Creation

New developments in the science of learning science of learning overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching - examples in history, mathematics and science teacher learning technology to support learning conclusions from new developments in the science of learning.

How people learn: Brain, mind, experience, and school.

/pdf/determining-lyapunov-exponents-from-a-time-series-3bxxq6d0wt.pdf

Determining Lyapunov exponents from a time series

A comprehensive review of spatiotemporal pattern formation in systems driven away from equilibrium is presented, with emphasis on comparisons between theory and quantitative experiments. Examples include patterns in hydrodynamic systems such as thermal convection in pure fluids and binary mixtures, Taylor-Couette flow, parametric-wave instabilities, as well as patterns in solidification fronts, nonlinear optics, oscillatory chemical reactions and excitable biological media. The theoretical starting point is usually a set of deterministic equations of motion, typically in the form of nonlinear partial differential equations. These are sometimes supplemented by stochastic terms representing thermal or instrumental noise, but for macroscopic systems and carefully designed experiments the stochastic forces are often negligible. An aim of theory is to describe solutions of the deterministic equations that are likely to be reached starting from typical initial conditions and to persist at long times. A unified description is developed, based on the linear instabilities of a homogeneous state, which leads naturally to a classification of patterns in terms of the characteristic wave vector q0 and frequency ω0 of the instability. Type Is systems (ω0=0, q0≠0) are stationary in time and periodic in space; type IIIo systems (ω0≠0, q0=0) are periodic in time and uniform in space; and type Io systems (ω0≠0, q0≠0) are periodic in both space and time. Near a continuous (or supercritical) instability, the dynamics may be accurately described via "amplitude equations," whose form is universal for each type of instability. The specifics of each system enter only through the nonuniversal coefficients. Far from the instability threshold a different universal description known as the "phase equation" may be derived, but it is restricted to slow distortions of an ideal pattern. For many systems appropriate starting equations are either not known or too complicated to analyze conveniently. It is thus useful to introduce phenomenological order-parameter models, which lead to the correct amplitude equations near threshold, and which may be solved analytically or numerically in the nonlinear regime away from the instability. The above theoretical methods are useful in analyzing "real pattern effects" such as the influence of external boundaries, or the formation and dynamics of defects in ideal structures. An important element in nonequilibrium systems is the appearance of deterministic chaos. A greal deal is known about systems with a small number of degrees of freedom displaying "temporal chaos," where the structure of the phase space can be analyzed in detail. For spatially extended systems with many degrees of freedom, on the other hand, one is dealing with spatiotemporal chaos and appropriate methods of analysis need to be developed. In addition to the general features of nonequilibrium pattern formation discussed above, detailed reviews of theoretical and experimental work on many specific systems are presented. These include Rayleigh-Benard convection in a pure fluid, convection in binary-fluid mixtures, electrohydrodynamic convection in nematic liquid crystals, Taylor-Couette flow between rotating cylinders, parametric surface waves, patterns in certain open flow systems, oscillatory chemical reactions, static and dynamic patterns in biological media, crystallization fronts, and patterns in nonlinear optics. A concluding section summarizes what has and has not been accomplished, and attempts to assess the prospects for the future.

/pdf/pattern-formation-outside-of-equilibrium-1601b9znum.pdf

Pattern formation outside of equilibrium

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

What do you do to start reading synergetics an introduction? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this synergetics an introduction.

Synergetics: An Introduction

Earlier experimental studies by one of us (Kelso, 1981a, 1984) have shown that abrupt phase transitions occur in human hand movements under the influence of scalar changes in cycling frequency. Beyond a critical frequency the originally prepared out-of-phase, antisymmetric mode is replaced by a symmetrical, in-phase mode involving simultaneous activation of homologous muscle groups. Qualitavely, these phase transitions are analogous to gait shifts in animal locomotion as well as phenomena common to other physical and biological systems in which new “modes” or spatiotemporal patterns arise when the system is parametrically scaled beyond its equilibrium state (Haken, 1983). In this paper a theoretical model, using concepts central to the interdisciplinary field of synergetics and nonlinear oscillator theory, is developed, which reproduces (among other features) the dramatic change in coordinative pattern observed between the hands.

A theoretical model of phase transitions in human hand movements

Synergetics, Introduction to.

1. Goal.- 1.1 Order and Disorder: Some Typical Phenomena.- 1.2 Some Typical Problems and Difficulties.- 1.3 How We Shall Proceed.- 2. Probability.- 2.1 Object of Our Investigations: The Sample Space.- 2.2 Random Variables.- 2.3 Probability.- 2.4 Distribution.- 2.5 Random Variables with Densities.- 2.6 Joint Probability.- 2.7 Mathematical Expectation E(X), and Moments.- 2.8 Conditional Probabilities.- 2.9 Independent and Dependent Random Variables.- 2.10 Generating Functions and Characteristic Functions.- 2.11 A Special Probability Distribution: Binomial Distribution.- 2.12 The Poisson Distribution.- 2.13 The Normal Distribution (Gaussian Distribution).- 2.14 Stirling's Formula.- 2.15 Central Limit Theorem.- 3. Information.- 3.1 Some Basic Ideas.- 3.2 Information Gain: An Illustrative Derivation.- 3.3 Information Entropy and Constraints.- 3.4 An Example from Physics: Thermodynamics.- 3.5 An Approach to Irreversible Thermodynamics.- 3.6 Entropy-Curse of Statistical Mechanics?.- 4. Chance.- 4.1 A Model of Brownian Movement.- 4.2 The Random Walk Model and Its Master Equation.- 4.3 Joint Probability and Paths. Markov Processes. The Chapman-Kolmogorov Equation. Path Integrals.- Sections with an asterisk in the heading may be omitted during a first reading..- 4.4 How to Use Joint Probabilities. Moments. Characteristic Function. Gaussian Processes.- 4.5 The Master Equation.- 4.6 Exact Stationary Solution of the Master Equation for Systems in Detailed Balance.- 4.7 The Master Equation with Detailed Balance. Symmetrization, Eigenvalues and Eigenstates.- 4.8 Kirchhoff's Method of Solution of the Master Equation.- 4.9 Theorems about Solutions of the Master Equation.- 4.10 The Meaning of Random Processes, Stationary State, Fluctuations, Recurrence Time.- 4.11 Master Equation and Limitations of Irreversible Thermodynamics.- 5. Necessity.- 5.1 Dynamic Processes.- 5.2 Critical Points and Trajectories in a Phase Plane. Once Again Limit Cycles.- 5.3 Stability.- 5.4 Examples and Exercises on Bifurcation and Stability.- 5.5 Classification of Static Instabilities, or an Elementary Approach to Thorn's Theory of Catastrophes.- 6. Chance and Necessity.- 6.1 Langevin Equations: An Example.- 6.2 Reservoirs and Random Forces.- 6.3 The Fokker-Planck Equation.- 6.4 Some Properties and Stationary Solutions of the Fokker-Planck-Equation.- 6.6 Time-Dependent Solutions of the Fokker-Planck Equation.- 6.6 Solution of the Fokker-Planck Equation by Path Integrals.- 6.7 Phase Transition Analogy.- 6.8 Phase Transition Analogy in Continuous Media: Space-Dependent Order Parameter.- 7. Self-Organization.- 7.1 Organization.- 7.2 Self-Organization.- 7.3 The Role of Fluctuations: Reliability or Adaptibility? Switching.- 7.4 Adiabatic Elimination of Fast Relaxing Variables from the Fokker-Planck Equation.- 7.5 Adiabatic Elimination of Fast Relaxing Variables from the Master Equation.- 7.6 Self-Organization in Continuously Extended Media. An Outline of the Mathematical Approach.- 7.7 Generalized Ginzburg-Landau Equations for Nonequilibrium Phase Transitions.- 7.8 Higher-Order Contributions to Generalized Ginzburg-Landau Equations.- 7.9 Scaling Theory of Continuously Extended Nonequilibrium Systems.- 7.10 Soft-Mode Instability.- 7.11 Hard-Mode Instability.- 8. Physical Systems.- 8.1 Cooperative Effects in the Laser: Self-Organization and Phase Transition.- 8.2 The Laser Equations in the Mode Picture.- 8.3 The Order Parameter Concept.- 8.4 The Single-Mode Laser.- 8.5 The Multimode Laser.- 8.6 Laser with Continuously Many Modes. Analogy with Superconductivity.- 8.7 First-Order Phase Transitions of the Single-Mode Laser.- 8.8 Hierarchy of Laser Instabilities and Ultrashort Laser Pulses.- 8.9 Instabilities in Fluid Dynamics: The Benard and Taylor Problems.- 8.10 The Basic Equations.- 8.11 The Introduction of New Variables.- 8.12 Damped and Neutral Solutions (R ? Rc).- 8.13 Solution Near R = Rc (Nonlinear Domain). Effective Langevin Equations.- 8.14 The Fokker-Planck Equation and Its Stationary Solution.- 8.15 A Model for the Statistical Dynamics of the Gunn Instability Near Threshold.- 8.16 Elastic Stability: Outline of Some Basic Ideas.- 9. Chemical and Biochemical Systems.- 9.1 Chemical and Biochemical Reactions.- 9.2 Deterministic Processes, Without Diffusion, One Variable.- 9.3 Reaction and Diffusion Equations.- 9.4 Reaction-Diffusion Model with Two or Three Variables: The Brusselator and the Oregonator.- 9.5 Stochastic Model for a Chemical Reaction Without Diffusion. Birth and Death Processes. One Variable.- 9.6 Stochastic Model for a Chemical Reaction with Diffusion. One Variable.- 9.7 Stochastic Treatment of the Brusselator Close to Its Soft-Mode Instability.- 9.8 Chemical Networks.- 10. Applications to Biology.- 10.1 Ecology, Population-Dynamics.- 10.2 Stochastic Models for a Predator-Prey System.- 10.3 A Simple Mathematical Model for Evolutionary Processes.- 10.4 A Model for Morphogenesis.- 10.5 Order Parameters and Morphogenesis.- 10.6 Some Comments on Models of Morphogenesis.- 11. Sociology and Economics.- 11.1 A Stochastic Model for the Formation of Public Opinion.- 11.2 Phase Transitions in Economics.- 12. Chaos.- 12.1 What is Chaos?.- 12.2 The Lorenz Model. Motivation and Realization.- 12.3 How Chaos Occurs.- 12.4 Chaos and the Failure of the Slaving Principle.- 12.5 Correlation Function and Frequency Distribution.- 12.6 Discrete Maps, Period Doubling, Chaos, Intermittency.- 13. Some Historical Remarks and Outlook.- References, Further Reading, and Comments.

Synergetics: An Introduction. Nonequilibrium Phase Transitions and Self- Organization in Physics, Chemistry and Biology

1. Introduction.- 1.1 What is Synergetics About?.- 1.2 Physics.- 1.2.1 Fluids: Formation of Dynamic Patterns.- 1.2.2 Lasers: Coherent Oscillations.- 1.2.3 Plasmas: A Wealth of Instabilities.- 1.2.4 Solid-State Physics: Multistability, Pulses, Chaos.- 1.3 Engineering.- 1.3.1 Civil, Mechanical, and Aero-Space Engineering: Post-Buckling Patterns, Flutter, etc.- 1.3.2 Electrical Engineering and Electronics: Nonlinear Oscillations.- 1.4 Chemistry: Macroscopic Patterns.- 1.5 Biology.- 1.5.1 Some General Remarks.- 1.5.2 Morphogenesis.- 1.5.3 Population Dynamics.- 1.5.4 Evolution.- 1.5.5 Immune System.- 1.6 Computer Sciences.- 1.6.1 Self-Organization of Computers, in Particular Parallel Computing.- 1.6.2 Pattern Recognition by Machines.- 1.6.3 Reliable Systems from Unreliable Elements.- 1.7 Economy.- 1.8 Ecology.- 1.9 Sociology.- 1.10 What are the Common Features of the Above Examples?.- 1.11 The Kind of Equations We Want to Study.- 1.11.1 Differential Equations.- 1.11.2 First-Order Differential Equations.- 1.11.3 Nonlinearity.- 1.11.4 Control Parameters.- 1.11.5 Stochasticity.- 1.11.6 Many Components and the Mezoscopic Approach.- 1.12 How to Visualize the Solutions.- 1.13 Qualitative Changes: General Approach.- 1.14 Qualitative Changes: Typical Phenomena.- 1.14.1 Bifurcation from One Node (or Focus) into Two Nodes (or Foci).- 1.14.2 Bifurcation from a Focus into a Limit Cycle (Hopf Bifurcation).- 1.14.3 Bifurcations from a Limit Cycle.- 1.14.4 Bifurcations from a Torus to Other Tori.- 1.14.5 Chaotic Attractors.- 1.14.6 Lyapunov Exponents *.- 1.15 The Impact of Fluctuations (Noise). Nonequilibrium Phase Transitions.- 1.16 Evolution of Spatial Patterns.- 1.17 Discrete Maps. The Poincare Map.- 1.18 Discrete Noisy Maps.- 1.19 Pathways to Self-Organization.- 1.19.1 Self-Organization Through Change of Control Parameters.- 1.19.2 Self-Organization Through Change of Number of Components.- 1.19.3 Self-Organization Through Transients.- 1.20 How We Shall Proceed.- 2. Linear Ordinary Differential Equations.- 2.1 Examples of Linear Differential Equations: The Case of a Single Variable.- 2.1.1 Linear Differential Equation with Constant Coefficient.- 2.1.2 Linear Differential Equation with Periodic Coefficient.- 2.1.3 Linear Differential Equation with Quasiperiodic Coefficient.- 2.1.4 Linear Differential Equation with Real Bounded Coefficient.- 2.2 Groups and Invariance.- 2.3 Driven Systems.- 2.4 General Theorems on Algebraic and Differential Equations.- 2.4.1 The Form of the Equations.- 2.4.2 Jordan's Normal Form.- 2.4.3 Some General Theorems on Linear Differential Equations.- 2.4.4 Generalized Characteristic Exponents and Lyapunov Exponents.- 2.5 Forward and Backward Equations: Dual Solution Spaces.- 2.6 Linear Differential Equations with Constant Coefficients.- 2.7 Linear Differential Equations with Periodic Coefficients.- 2.8 Group Theoretical Interpretation.- 2.9 Perturbation Approach*.- 3. Linear Ordinary Differential Equations with Quasiperiodic Coefficients*.- 3.1 Formulation of the Problem and of Theorem 3.- 3.2 Auxiliary Theorems (Lemmas).- 3.3 Proof of Assertion (a) of Theorem 3.1.1: Construction of a Triangular Matrix: Example of a 2 x 2 Matrix.- 3.4 Proof that the Elements of the Triangular Matrix C are Quasiperiodic in ? (and Periodic in ?j and Ck with Respect to ?: Exampleof a 2 x 2 Matrix.- 3.5 Construction of the Triangular Matrix C and Proof that Its Elements are Quasiperiodic in ? (and Periodic in ?j and Ck with Respect to ?): The Case of an m x m Matrix, all A's Different.- 3.6 Approximation Methods. Smoothing.- 3.6.1 A Variational Method.- 3.6.2 Smoothing.- 3.7 The Triangular Matrix C and Its Reduction.- 3.8 The General Case: Some of the Generalized Characteristic Exponents Coincide.- 3.9 Explicit Solution of (3.1.1) by an Iteration Procedure.- 4. Stochastic Nonlinear Differential Equations.- 4.1 An Example.- 4.2 The Ito Differential Equation and the Ito-Fokker-Planck Equation.- 4.3 The Stratonovich Calculus.- 4.4 Langevin Equations and Fokker-Planck Equation.- 5. The World of Coupled Nonlinear Oscillators.- 5.1 Linear Oscillators Coupled Together.- 5.1.1 Linear Oscillators with Linear Coupling.- 5.1.2 Linear Oscillators with Nonlinear Coupling. An Example. Frequency Shifts.- 5.2 Perturbations of Quasiperiodic Motion for Time-Independent Amplitudes (Quasiperiodic Motion Shall Persist).- 5.3 Some Considerations on the Convergence of the Procedure*.- 6. Nonlinear Coupling of Oscillators: The Case of Persistence of Quasiperiodic Motion.- 6.1 The Problem.- 6.2 Moser's Theorem (Theorem 6.2.1).- 6.3 The Iteration Procedure*.- 7. Nonlinear Equations. The Slaving Principle.- 7.1 An Example.- 7.1.1 The Adiabatic Approximation.- 7.1.2 Exact Elimination Procedure.- 7.2 The General Form of the Slaving Principle. Basic Equations.- 7.3 Formal Relations.- 7.4 The Iteration Procedure.- 7.5 An Estimate of the Rest Term. The Question of Differentiability.- 7.6 Slaving Principle for Discrete Noisy Maps*.- 7.7 Formal Relations*.- 7.8 The Iteration Procedure for the Discrete Case*.- 7.9 Slaving Principle for Stochastic Differential Equations*.- 8. Nonlinear Equations. Qualitative Macroscopic Changes.- 8.1 Bifurcations from a Node or Focus. Basic Transformations.- 8.2 A Simple Real Eigenvalue Becomes Positive.- 8.3 Multiple Real Eigenvalues Become Positive.- 8.4 A Simple Complex Eigenvalue Crosses the Imaginary Axis. Hopf Bifurcation.- 8.5 Hopf Bifurcation, Continued.- 8.6 Frequency Locking Between Two Oscillators.- 8.7 Bifurcation from a Limit Cycle.- 8.8 Bifurcation from a Limit Cycle: Special Cases.- 8.8.1 Bifurcation into Two Limit Cycles.- 8.8.2 Period Doubling.- 8.8.3 Subharmonics.- 8.8.4 Bifurcation to a Torus.- 8.9 Bifurcation from a Torus (Quasiperiodic Motion).- 8.10 Bifurcation from a Torus: Special Cases.- 8.10.1 A Simple Real Eigenvalue Becomes Positive.- 8.10.2 A Complex Nondegenerate Eigenvalue Crosses the Imaginary Axis.- 8.11 Instability Hierarchies, Scenarios, and Routes to Turbulence.- 8.11.1 The Landau-Hopf Picture.- 8.11.2 The Ruelle and Takens Picture.- 8.11.3 Bifurcations of Tori. Quasiperiodic Motions.- 8.11.4 The Period-Doubling Route to Chaos. Feigenbaum Sequence.- 8.11.5 The Route via Intermittency.- 9. Spatial Patterns.- 9.1 The Basic Differential Equations.- 9.2 The General Method of Solution.- 9.3 Bifurcation Analysis for Finite Geometries.- 9.4 Generalized Ginzburg-Landau Equations.- 9.5 A Simplification of Generalized Ginzburg-Landau Equations. Pattern Formation in Benard Convection.- 10. The Inclusion of Noise.- 10.1 The General Approach.- 10.2 A Simple Example.- 10.3 Computer Solution of a Fokker-Planck Equation for a Complex Order Parameter.- 10.4 Some Useful General Theorems on the Solutions of Fokker-Planck Equations.- 10.4.1 Time-Dependent and Time-Independent Solutions of the Fokker-Planck Equation, if the Drift Coefficients are Linear in the Coordinates and the Diffusion Coefficients Constant.- 10.4.2 Exact Stationary Solution of the Fokker-Planck Equation for Systems in Detailed Balance.- 10.4.3 AnExample.- 10.4.4 Useful Special Cases.- 10.5 Nonlinear Stochastic Systems Close to Critical Points: A Summary.- 11. Discrete Noisy Maps.- 11.1 Chapman-Kolmogorov Equation.- 11.2 The Effect of Boundaries. One-Dimensional Example.- 11.3 Joint Probability and Transition Probability. Forward and Backward Equation 305.- 11.4 Connection with Fredholm Integral Equation 306.- 11.5 Path Integral Solution 307.- 11.6 The Mean First Passage Time 308.- 11.7 Linear Dynamics and Gaussian Noise. Exact Time-Dependent Solution of the Chapman-Kolmogorov Equation 310.- 12. Example of an Unsolvable Problem in Dynamics.- 13. Some Comments on the Relation Between Synergetics and Other Sciences.- Appendix A: Moser's Proof of His Theorem.- A.1 Convergence of the Fourier Series.- A.2 The Most General Solution to the Problem of Theorem.- A.3 Convergent Construction.- A.4 Proof of Theorem.- References.

Hermann Haken

Papers

Synergetics: An Introduction

A theoretical model of phase transitions in human hand movements

Synergetics, Introduction to.

Synergetics: An Introduction. Nonequilibrium Phase Transitions and Self- Organization in Physics, Chemistry and Biology

Advanced Synergetics: Instability Hierarchies of Self-Organizing Systems and Devices