| The Challenge of Complex Systems | p. 1 |
| What Are Complex Systems? | p. 1 |
| How to Deal with Complex Systems | p. 5 |
| Model Systems | p. 7 |
| Self-Organization | p. 10 |
| Aiming at University | p. 11 |
| Thermodynamics | p. 11 |
| Statistical Physics | p. 12 |
| Synergetics | p. 13 |
| Information | p. 14 |
| Shannon Information: Meaning Exorcised | p. 15 |
| Effects of Information | p. 16 |
| Self-Creation of Meaning | p. 23 |
| How Much Information Do We Need to Maintain an Ordered State? | p. 29 |
| The Second Foundation of Synergetics | p. 33 |
| From the Microscopic to the Macroscopic World | p. 36 |
| Levels of Description | p. 36 |
| Langevin Equations | p. 37 |
| Fokker-Planck Equation | p. 40 |
| Exact Stationary Solution of the Fokker-Planck Equation for Systems in Detailed Balance | p. 41 |
| Detailed Balance | p. 41 |
| The Required Structure of the Fokker-Planck Equation and Its Stationary Solution | p. 42 |
| Path Integrals | p. 44 |
| Reduction of Complexity, Order Parameters and the Slaving Principle | p. 45 |
| Linear Stability Analysis | p. 46 |
| Transformation of Evolution Equations | p. 47 |
| The Slaving Principle | p. 48 |
| Nonequilibrium Phase Transitions | p. 49 |
| Pattern Formation | p. 51 |
| ... and Back Again: The Maximum Information Principle (MIP) | p. 53 |
| Some Basic Ideas | p. 53 |
| Information Gain | p. 57 |
| Information Entropy and Constraints | p. 58 |
| Continuous Variables | p. 63 |
| An Example from Physics: Thermodynamics | p. 65 |
| Application of the Maximum Information Principle to Self-Organizing Systems | p. 69 |
| Introduction | p. 69 |
| Application to Self-Organizing Systems: Single Mode Laser | p. 69 |
| Multimode Laser Without Phase Relations | p. 71 |
| Processes Periodic in Order Parameters | p. 72 |
| The Maximum Information Principle for Nonequilibrium Phase Transitions: Determination of Order Parameters, Enslaved Modes, and Emerging Patters | p. 74 |
| Introduction | p. 74 |
| General Approach | p. 74 |
| Determination of Order Parameters, Enslaved Modes, and Emerging Patterns | p. 76 |
| Approximations | p. 77 |
| Spatial Patterns | p. 78 |
| Relation to the Landau Theory of Phase Transitions. Guessing of Fokker-Planck Equations | p. 79 |
| Information, Information Gain, and Efficiency of Self-Organizing Systems Close to Their Instability Points | p. 81 |
| Introduction | p. 81 |
| The Slaving Principle and Its Application to Information | p. 82 |
| Information Gain | p. 82 |
| An Example: Nonequilibrium Phase Transitions | p. 83 |
| Soft Single-Mode Instabilities | p. 84 |
| Can We Measure the Information and the Information Gain? | p. 85 |
| Efficiency | p. 85 |
| Information and Information Gain | p. 86 |
| Several Order Parameters | p. 87 |
| Explicit Calculation of the Information of a Single Order Parameter | p. 88 |
| The Region Well Below Threshold | p. 89 |
| The Region Well Above Threshold | p. 90 |
| Numerical Results | p. 93 |
| Discussion | p. 94 |
| Exact Analytical Results on Information, Information Gain, and Efficiency of a Single Order Parameter | p. 95 |
| The Instability Point | p. 97 |
| The Approach to Instability | p. 98 |
| The Stable Region | p. 99 |
| The Injected Signal | p. 100 |
| Conclusions | p. 101 |
| The S-Theorem of Klimontovich | p. 102 |
| Region 1: Below Laser Threshold | p. 104 |
| Region 2: At Threshold | p. 104 |
| Region 3: Well Above Threshold | p. 105 |
| The Contribution of the Enslaved Modes to the Information Close to Nonequilibrium Phase Transitions | p. 107 |
| Direct Determination of Lagrange Multipliers | p. 115 |
| Information Entropy of Systems Below and Above Their Critical Point | p. 115 |
| Direct Determination of Lagrange Multipliers Below, At and Above the Critical Point | p. 117 |
| Unbiased Modeling of Stochastic Processes: How to Guess Path Integrals, Fokker-Planck Equations and Langevin-Ito Equations | p. 125 |
| One-Dimensional State Vector | p. 125 |
| Generalization to a Multidimensional State Vector | p. 127 |
| Correlation Functions as Constraints | p. 130 |
| The Fokker-Planck Equation Belonging to the Short-Time Propagator | p. 132 |
| Can We Derive Newton's Law from Experimental Data? | p. 133 |
| Application to Some Physical Systems | p. 135 |
| Multimode Lasers with Phase Relations | p. 135 |
| The Single-Mode Laser Including Polarization and Inversion | p. 136 |
| Fluid Dynamics: The Convection Instability | p. 138 |
| Transitions Between Behavioral Patterns in Biology, An Example: Hand Movements | p. 140 |
| Some Experimental Facts | p. 140 |
| How to Model the Transition | p. 141 |
| Critical Fluctuations | p. 147 |
| Some Conclusions | p. 151 |
| Pattern Recognition. Unbiased Guesses of Processes: Explicit Determination of Lagrange Multipliers | p. 153 |
| Feature Selection | p. 153 |
| An Algorithm for Pattern Recognition | p. 159 |
| The Basic Construction Principle of a Synergetic Computer | p. 161 |
| Learning by Means of the Information Gain | p. 163 |
| Processes and Associative Action | p. 165 |
| Explicit Determination of the Lagrange Multipliers of the Conditional Probability. General Approach for Discrete and Continuous Processes | p. 169 |
| Approximation and Smoothing Schemes. Additive Noise | p. 174 |
| An Explicit Example: Brownian Motion | p. 181 |
| Approximation and Smoothing Schemes, Multiplicative (and Additive) Noise | p. 184 |
| Explicit Calculation of Drift and Diffusion Coefficients. Examples | p. 185 |
| Process Modelling, Prediction and Control, Robotics | p. 187 |
| Non-Markovian Processes. Connection with Chaos Theory | p. 189 |
| Checking the Markov Property | p. 189 |
| Time Series Analysis | p. 190 |
| Information Compression in Cognition: The Interplay between Shannon and Semantic Information | p. 195 |
| Information Compression: A General Formula | p. 195 |
| Pattern Recognition as Information Compression: Use of Symmetries | p. 197 |
| Deformations | p. 199 |
| Reinterpretation of the Results of Sects. 13.1-13.3 | p. 201 |
| Quantum Systems | p. 203 |
| Why Quantum Theory of Information? | p. 203 |
| The Maximum Information Principle | p. 205 |
| Order Parameters, Enslaved Modes and Patterns | p. 211 |
| Information of Order Parameters and Enslaved Modes | p. 214 |
| Quantum Information | p. 216 |
| Basic Concepts of Quantum Information. Q-bits | p. 216 |
| Phase and Decoherence | p. 218 |
| Representation of Numbers | p. 219 |
| Register | p. 220 |
| Entanglement | p. 221 |
| Quantum Computation | p. 222 |
| Classical Gates | p. 222 |
| Quantum Gates | p. 223 |
| Calculation of the Period of a Sequence by a Quantum Computer | p. 227 |
| Coding, Decoding and Breaking Codes | p. 229 |
| A Little Mathematics | p. 230 |
| RSA Coding and Decoding | p. 230 |
| Shor's Approach, Continued | p. 231 |
| The Physics of Spin 1/2 | p. 233 |
| Quantum Theory of a Spin in Mutually Perpendicular Magnetic Fields, One Constant and One Time Dependent | p. 235 |
| Quantum Computation and Self-Organization | p. 241 |
| Concluding Remarks and Outlook | p. 242 |
| References | p. 244 |
| Subject Index | p. 251 |
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