Selforganization

The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns--phenomena that have fascinated naturalists for centuries--a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology--a field of study at the forefront of life sciences research.

The Self-Organizing Economy In the last few years the concept of self-organizing systems—complex systems in which randomness and chaos seem spontaneously to evolve into unexpected order—has linked together researchers in many fields, from artificial intelligence to chemistry, from evolution to geology. Now leading economist Paul Krugman shows how principles that explain the growth of hurricanes and embryos can also explain the formation of cities and business cycles; how the same principles of “order from random growth” can explain the strangely simple rules that describe the sizes of earthquakes, meteorites, and metropolitan areas. Weaving together strands from many disciplines, from location theory to biology, The Self-Organizing Economy offers a surprising new view of how the economy structures itself in space and time.

During the past twenty years, a broad spectrum of theories and methods have been developed in physics, chemistry and molecular biology to explain structure formation in complex systems. These methods have been applied to many different fields such as economics, sociology and town planning, and this book reflects the interdisciplinary nature of complexity and self-organisation. The main focus is on the emergence of collective phenomena from individual or microscopic interactions. Presents a wide-ranging overview from fundamental aspects of the evolution of complexity, to applications in biology, ecology, sociology, economics, and urban structure formation.

Technological systems become organized by commands from outside, as when human intentions lead to the building of structures or machines. But many nat ural systems become structured by their own internal processes: these are the self organizing systems, and the emergence of order within them is a complex phe nomenon that intrigues scientists from all disciplines. Unfortunately, complexity is ill-defined. Global explanatory constructs, such as cybernetics or general sys tems theory, which were intended to cope with complexity, produced instead a grandiosity that has now, mercifully, run its course and died. Most of us have become wary of proposals for an "integrated, systems approach" to complex matters; yet we must come to grips with complexity some how. Now is a good time to reexamine complex systems to determine whether or not various scientific specialties can discover common principles or properties in them. If they do, then a fresh, multidisciplinary attack on the difficulties would be a valid scientific task. Believing that complexity is a proper scientific issue, and that self-organizing systems are the foremost example, R. Tomovic, Z. Damjanovic, and I arranged a conference (August 26-September 1, 1979) in Dubrovnik, Yugoslavia, to address self-organizing systems. We invited 30 participants from seven countries. Included were biologists, geologists, physicists, chemists, mathematicians, bio physicists, and control engineers. Participants were asked not to bring manu scripts, but, rather, to present positions on an assigned topic. Any writing would be done after the conference, when the writers could benefit from their experi ences there.

The widespread interest this book has found among professors, scientists and stu dents working in a variety of fields has made a new edition necessary. I have used this opportunity to add three new chapters on recent developments. One of the most fascinating fields of modern science is cognitive science which has become a meet ing place of many disciplines ranging from mathematics over physics and computer science to psychology. Here, one of the important links between these fields is the concept of information which, however, appears in various disguises, be it as Shan non information or as semantic information (or as something still different). So far, meaning seemed to be exorcised from Shannon information, whereas meaning plays a central role in semantic (or as it is sometimes called "pragmatic") information. In the new chapter 13 it will be shown, however, that there is an important interplay between Shannon and semantic information and that, in particular, the latter plays a decisive role in the fixation of Shannon information and, in cognitive processes, al lows a drastic reduction of that information. A second, equally fascinating and rapidly developing field for mathematicians, computer scientists and physicists is quantum information and quantum computa tion. The inclusion of these topics is a must for any modern treatise dealing with in formation. It becomes more and more evident that the abstract concept of informa tion is inseparably tied up with its realizations in the physical world.

This invaluable book is the first of its kind on "selforganizology", the science of self-organization. It covers a wide range of topics, such as the theory, principle and methodology of selforganizology, agent-based modelling, intelligence basis, ant colony optimization, fish/particle swarm optimization, cellular automata, spatial diffusion models, evolutionary algorithms, self-adaptation and control systems, self-organizing neural networks, catastrophe theory and methods, and self-organization of biological communities, etc. Readers will have an in-depth and comprehensive understanding of selforganizology, with detailed background information provided for those who wish to delve deeper into the subject and explore research literature. This book is a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences. Contents:Organization and Organizational TheorySelforganizology: The Science of Self-organizationAgent-based ModelingIntelligence PrinciplesCatastrophe Theory and MethodsSelf-adaptation and Control SystemsCellular Automata and Spatial Diffusion ModelsArtificial Neural NetworksAnt Colony OptimizationFish and Particle Swarm OptimizationSynergy, Coevolution, and Evolutionary AlgorithmsSynergy: Correlation AnalysisCommunity Succession and AssemblyMathematical Foundations Readership: Research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences. Key Features:This book is the first monograph on "selforganizology" in the world, with no competing titlesThe author is a prominent scientist, who is the editor-in-chief of six international journals, including SelforganizologyKeywords:Selforganizology;Self-organization;Agent-based Modeling;Algorithms;Computational Intelligence;Ant Colony Optimization;Evolutionary Algorithms;Cellular Automata;Particle Swarm Optimization;Catastrophe Theory

Self-Organization in Sensor and Actor Networks explores self-organization mechanisms and methodologies concerning the efficient coordination between intercommunicating autonomous systems.Self-organization is often referred to as the multitude of algorithms and methods that organise the global behaviour of a system based on inter-system communication. Studies of self-organization in natural systems first took off in the 1960s. In technology, such approaches have become a hot research topic over the last 4-5 years with emphasis upon management and control in communication networks, and especially in resource-constrained sensor and actor networks. In the area of ad hoc networks new solutions have been discovered that imitate the properties of self-organization. Some algorithms for on-demand communication and coordination, including data-centric networking, are well-known examples. Key features include: Detailed treatment of self-organization, mobile sensor and actor networks, coordination between autonomous systems, and bio-inspired networking. Overview of the basic methodologies for self-organization, a comparison to central and hierarchical control, and classification of algorithms and techniques in sensor and actor networks. Explanation of medium access control, ad hoc routing, data-centric networking, synchronization, and task allocation issues. Introduction to swarm intelligence, artificial immune system, molecular information exchange. Numerous examples and application scenarios to illustrate the theory. Self-Organization in Sensor and Actor Networks will prove essential reading for students of computer science and related fields; researchers working in the area of massively distributed systems, sensor networks, self-organization, and bio-inspired networking will also find this reference useful.

For centuries, humankind has believed that the world with all its form and structure was created by supernatural forces. In recent decades science has shaken these beliefs with the discovery of the exciting possibility of a self-created and self-creating world — of self-organization. Synergetics endeavours to reveal the intimate mechanisms of self-organization. The transitions from chaos to order, the nature of self-organization, the various approaches to it and certain philosophical inferences are outlined. Synergetics thus represents a remarkable confluence of many strands of thought, and has become a paradigm in modern culture. This book exposes the reader to striking new vistas in physics and mathematics, chemistry and biology, social sciences and philosophy — all interlocked around the concept of self-organization.