Complexity Explained - Erdi, Peter

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Résumé :
This book is, of course about complexity. The title of the book, as you may recognize was motivated (excuse me for using this very mild expression) by Daniel Dennett?s Consciousness Explained [130]. Dennett?s intention was to explain consciousness as the emergent product of the interaction among c- stituents having physical and neural character. The goal of this book is to explain how various types of complexity emerge due to the interaction among constituents. There are many questions to be answered, how to understand, control, decompose, manage, predict the many-faced complexity. After tea- ing thissubjectforseveralyearsIfeelthatthe time hascome toputthe whole story together. The term ?complex system? is a buzzword, but we certainly don?t have a single de?nition for it. There are several predominant features of compl- ity. Complex processes may show unpredictable behavior (which we still try to predict somehow), may lead to uncontrolled explosion (such in case of epilepsy, earthquake eruptions or stock market crashes). One of the char- teristic feature of simple systems is, that there is a single cause which implies a single e?ect. For large class of complex systems it is true that e?ects are fed back to modify causes. Biological cells belong to this class. Furthermore they are open to material, energetic and information ?ow by interaction with their environment, still they are organizationallyclosed units. Another aspect of complexity is the question how collective phenomena emerge by some se- organized mechanisms.

Sommaire:
1 COMPLEX SYSTEMS: THE INTELLECTUAL LANDSCAPE 1.1 The century of complexity? 1.2 Characteristics of simple and complex systems 1.2.1 System and its environment 1.2.2 Simple systems 1.2.3 Complex systems 1.3 Connecting the dots 2 HISTORY of COMPLEX SYSTEMS RESEARCH 2.1 Reductionist success stories versus the importance of organization principles 2.1.1 Reductionism and holism in quantum physics 2.1.2 Reductionism and complexity in molecular biology 2.2 Ancestors of present day complex system research 2.2.1 Systems theory 2.2.2 Cybernetics 2.2.3 Nonlinear science in action: Theory of dissipative structures, synergetics and catastrophe theory 3 FROM THE CLOCKWORK WORLD VIEW to IRREVERSIBILITY (and BACK?) 3.1 Cyclic universe versus linear time concept: the metaphysical perspective 3.1.1 Cyclic Universe 3.1.2 Linear time concepts 3.2 The Newtonian Clockwork Universe 3.2.1 The mechanical clock 3.2.2 Kepler?s integral laws 3.2.3 Newton?s differential laws, Hamilton equations, conservative oscillation, dissipation 3.3 Mechanics versus Thermodynamics 3.3.1 Heat conduction and irreversibility 3.3.2 Steam engine, feedback control, irreversibility 3.3.3 The first and second laws of thermodynamics 3.4 The birth of the modern theory of dynamical systems 3.5 Oscillations 3.5.1 The Lotka ?Volterra Model 3.5.2 Stable oscillation: limit cycles 3.5.3 Quasiperiodic motions: A few words about the modern theory of dynamical systems 3.6 The chaos paradigm: then and now 3.6.1 Defining and detecting chaos 3.6.2 Structural and geometrical conditions of chaos: what is important and what is not? 3.6.3 The necessity of being chaotic 3.6.4 Controlling chaos: why and how? 3.6.5 Traveling to High-dimension land: Chaotic itinerancy 3.7 Direction of evolution 3.7.1 Doll?s law in retrospective 3.7.2 Is something never-decreasing during evolution? 3.8 Cyclic universe: revisited. . . and criticized 4 THE DYNAMIC WORLD VIEW in ACTION 4.1 Causality, teleology and about the scope and limits of the dynamical paradigm 4.1.1 Causal versus teleological description 4.1.2 Causality, networks, emergent novelty 4.2 Chemical kinetics: a prototype of nonlinear science 4.2.1 On the structure ? dynamics relationship for chemical reactions 4.2.2 Chemical kinetics as a metalanguage 4.2.3 Spatiotemporal patterns in chemistry and biology 4.3 Systems biology: the half admitted renaissance of cybernetics and systems theory 4.3.1 Life itself 4.3.2 Cells as self-referential systems 4.3.3 The old-new systems biology 4.3.4 Random Boolean networks: model framework and applications for genetic networks 4.4 Population dynamic and epidemic models: biological and social 4.4.1 Connectivity, stability, diversity 4.4.2 The epidemic propagation of infections and ideas 4.4.3 Modeling social epidemics 4.5 Evolutionary dynamics 4.6 Dynamic models of war and love 4.6.1 Lanchaster?s combat model and its variations 4.6.2 Is love different from war? 4.7 Social dynamics: some examples 4.7.1 Segregation dynamics 4.7.2 Opinion dynamics 4.8 Nonlinear dynamics in economics: some examples 4.8.1 Business cycles 4.8.2 Controlling chaos in economic models 4.9 Drug market: controlling chaos 5 THE SEARCH FOR LAWS: DEDUCTIVE VERSUS INDUCTIVE 5.